RESEARCH CENTRE

Inria Centre
at Université de Lorraine

IN PARTNERSHIP WITH:

CNRS, Université de Lorraine

2023
ACTIVITY REPORT

Project-Team

SEMAGRAMME

Semantic Analysis of Natural Language

IN COLLABORATION WITH: Laboratoire lorrain de recherche en
informatique et ses applications (LORIA)

DOMAIN

Perception, Cognition and Interaction

THEME

Language, Speech and Audio

Contents

Project-Team SEMAGRAMME

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Scientific Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Syntax-Semantics Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Discourse Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Common Basic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Formal Language Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Symbolic Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Type Theory and Typed Lambda-Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Deep Semantic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Text Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Types for discourse markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

7 New software, platforms, open data

7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 ACGtk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 Grew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.3 SLODiM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.4 HostoMytho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.5 Arborator-Grew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.1 Morphosyntactic Treebanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 New results

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8.1 Syntax-Semantics Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1.1 Abstract Categorial Grammars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1.2 Formal semantics of dependency relations . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1.3 Lexical Semantics and Linguistic Knowledge . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1.4 Semantic Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.1.5 Semantics of questions
8.2 Discourse Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.2.1 Dialogue Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.2.2 Discourse Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.2.3 Pathological Discourse Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.2.4 Cognitive traces of side issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.3 Common Basic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.3.1 Universal Dependencies and Surface Syntactic Universal Dependencies . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . 15
8.3.2 Multiword annotation in the Parseme project
8.3.3 Induction of Descriptive Grammars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.3.4 Mapping Lexical Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3.5 Sentence Semantic Similarity Corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.4 Ethics and biases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

8.4.1 Ethics@Loria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.4.2 Evaluating Stereotypes in Masked Language Models in Many Languages . . . . . . . 16
8.4.3 Evaluating stereotypes in autoregressive language models . . . . . . . . . . . . . . . . 16
8.4.4 NLP for NLP and Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
8.4.5 Ethics in AI Integration into Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

9 Bilateral contracts and grants with industry

17
9.1 Bilateral Grants with Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
9.1.1 Yseop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

10 Partnerships and cooperations

10.2 European initiatives

10.1 International research visitors

10.1.1 Visits to international teams

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2.1 H2020 projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2.2 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.3.1 ANR Project: InExtenso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.3.2 ANR Project: CoDeinE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.3.3 ANR Project: Autogramm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.3.4 ANR Project: CODIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

11 Dissemination

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11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.3 Journal
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.5 Invited seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.6 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.7 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1.8 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3.1 Internal or external Inria responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3.2 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3.4 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

12 Scientific production

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12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
12.2 Publications of the year
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12.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
12.4 Cited publications

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Project-Team SEMAGRAMME

Creation of the Project-Team: 2013 July 01

Keywords

Computer sciences and digital sciences

A5.8. – Natural language processing

A7.2. – Logic in Computer Science

A9.4. – Natural language processing

Other research topics and application domains

B2. – Health

B9.6.8. – Linguistics

B9.9. – Ethics

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Philippe de Groote [Team leader, INRIA, Senior Researcher]

• Bruno Guillaume [INRIA, Researcher]

• Sylvain Pogodalla [INRIA, Researcher]

Faculty Members

• Maxime Amblard [UL, Professor, HDR]

• Karën Fort [SORBONNE UNIVERSITE, Associate Professor, UL appointed, HDR]

• Jacques Jayez [ENS DE LYON, Emeritus, UL appointed]

• Michel Musiol [UL, Professor Delegation, from Sep 2023, HDR]

• Guy Perrier [UL, Emeritus, until Jun 2023, HDR]

Post-Doctoral Fellow

• Marc Anderson [UL, Post-Doctoral Fellow]

PhD Students

• Samuel Buchel [UL, until Sep 2023]

• Hee-Soo Choi [UL]

• Marie Cousin [UL]

• Amandine Decker [UL, University of Gothenburg]

• Fanny Ducel [UNIV PARIS SACLAY, from Oct 2023]

• Maxime Guillaume [Yseop, CIFRE]

• Amandine Lecomte [INRIA]

• Chuyuan Li [UL, ATER, until Aug 2023]

• Pierre Ludmann [Polytech Nancy (PRCE), until Sep 2023]

• Siyana Pavlova [UL, ATER, from Sep 2023]

• Siyana Pavlova [UL, until Aug 2023]

• Valentin Richard [UL]

• Priyansh Trivedi [INRIA, until May 2023]

Technical Staff

• Khensa Amani Daoudi [INRIA, Engineer, from Feb 2023]

• Bertrand Remy [UL, Engineer, from Feb 2023, ANR Codeine ANR-20-CE23-0026)]

• Vincent Tourneur [INRIA, Engineer]

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Interns and Apprentices

• Karolina Boczon [UL, Intern, from Feb 2023 until Aug 2023]

• Omar Cherif [INRIA, Intern, from Mar 2023 until Sep 2023]

• Fanny Ducel [UL, Intern, from Mar 2023 until Sep 2023]

• Julie Halbout [INRIA, Intern, from Feb 2023 until Jul 2023]

• Laura Masson-Grehaigne [UL, Intern, from May 2023 until Jul 2023]

• Violette Pelgrims [UL, Intern, until Aug 2023]

• Jules Steelandt [UL, Intern, from Feb 2023 until Apr 2023, ANR Codeine ANR-20-CE23-0026]

Administrative Assistants

• Angelique Marc [INRIA, from Sep 2023]

• Anne-Marie Messaoudi [UL]

External Collaborators

• Mathieu Constant [UL, HDR]

• Chuyuan Li [British Columbia University, from Sep 2023]

• Michel Musiol [UL, until Aug 2023, HDR]

2 Overall objectives

2.1 Scientific Context

Computational linguistics is a discipline at the intersection of computer science and linguistics. On the
theoretical side, it aims to provide computational models of the human language faculty. On the applied
side, it is concerned with natural language processing and its practical applications.

From a structural point of view, linguistics is traditionally organized into the following sub-fields:

• Phonology, the study of language abstract sound systems.

• Morphology, the study of word structure.

• Syntax, the study of language structure, i.e., the way words combine into grammatical phrases and

sentences.

• Semantics, the study of meaning at the levels of words, phrases, and sentences.

• Pragmatics, the study of the ways in which the meaning of an utterance is affected by its context.

Computational linguistics is concerned by all these fields. Consequently, various computational
models, whose application domains range from phonology to pragmatics, have been developed. Among
these, logic-based models play an important part, especially at the “highest” levels.

At the level of syntax, generative grammars may be seen as basic inference systems, while categorial
grammars are based on substructural logics specified by Gentzen sequent calculi. Finally, model-theoretic
grammars amount to sets of logical constraints to be satisfied.

At the level of semantics, the most common approaches derive from Montague grammars, which are
based on the simply typed λ-calculus and Church’s simple theory of types. In addition, various logics
(modal, hybrid, intensional, higher order...) are used to express logical semantic representations.

At the level of pragmatics, the situation is less clear. The word pragmatics has been introduced by
Morris to designate the branch of philosophy of language that studies, besides linguistic signs, their

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relation to their users and the possible contexts of use. The definition of pragmatics was not quite
precise, and, for a long time, several authors have considered (and some authors are still considering)
pragmatics as the wastebasket of syntax and semantics. Nevertheless, as far as discourse processing is
concerned (which includes pragmatic problems such as pronominal anaphora resolution), logic-based
approaches have also been successful. In particular, Kamp’s Discourse Representation Theory gave rise
to sophisticated ‘dynamic’ logics. The situation, however, is less satisfactory than it is at the semantic
level. On the one hand, we are facing a kind of logical “tower of Babel”. The various pragmatic logic-based
models that have been developed, while sharing underlying mathematical concepts, differ in several
respects and are too often based on ad hoc features. As a consequence, they are difficult to compare and
appear more as competitors than as collaborative theories that could be integrated. On the other hand,
several phenomena related to discourse dynamics (e.g., context updating, presupposition projection
and accommodation, contextual reference resolution...) are still lacking deep logical explanations. We
strongly believe, however, that this situation can be improved by applying to pragmatics the same
approach Montague applied to semantics, using the standard tools of mathematical logic.

Accordingly:

The overall objective of the Sémagramme project is to design and develop new unifying logic-
based models, methods, and tools for the semantic analysis of natural language utterances and
discourses. This includes the logical modeling of pragmatic phenomena related to discourse
dynamics. Typically, these models and methods will be based on standard logical concepts
(stemming from formal language theory, mathematical logic, and type theory), which should
make them easy to integrate.

The project is organized along three research directions (i.e., syntax-semantics interface, discourse

dynamics, and common basic resources), which interact as explained below.

Moreover, a transversal and transdisciplinary theme has been developed in the team in the past years:

ethics in NLP and more generally in AI.

2.2 Syntax-Semantics Interface

The Sémagramme project intends to focus on the semantics of natural languages (in a wider sense than
usual, including some pragmatics). Nevertheless, the semantic construction process is syntactically
guided, that is, the constructions of logical representations of meaning are based on the analysis of the
syntactic structures. We do not want, however, to commit ourselves to such or such specific theory of
syntax. Consequently, our approach should be based on an abstract generic model of the syntax-semantic
interface.

Here, an important idea of Montague comes into play, namely, the “homomorphism requirement”:
semantics must appear as a homomorphic image of syntax. While this idea is almost a truism in the
context of mathematical logic, it remains challenged in the context of natural languages. Nevertheless,
Montague’s idea has been quite fruitful, especially in the field of categorial grammars, where van Benthem
showed how syntax and semantics could be connected using the Curry-Howard isomorphism. This
correspondence is the keystone of the syntax-semantics interface of modern type-logical grammars. It
also motivated the definition of our own Abstract Categorial Grammars [56].

Technically, an Abstract Categorial Grammar simply consists of a (linear) homomorphism between
two higher-order signatures. Extensive studies have shown that this simple model allows several gram-
matical formalisms to be expressed, providing them with a syntax-semantics interface for free [57],
[7].

We intend to carry on with the development of the Abstract Categorial Grammar framework. At the
foundational level, we will define and study possible type theoretic extensions of the formalism, in order
to increase its expressive power and its flexibility. At the implementation level, we will continue the
development of an Abstract Categorial Grammar support system.

As said above, considering the syntax-semantics interface as the starting point of our investigations
allows us not to be committed to some specific syntactic theory. The Montagovian syntax-semantics
interface, however, cannot be considered to be universal. In particular, it does not seem to be well adapted
to dependency and model-theoretic grammars. Consequently, in order to be as generic as possible, we
intend to explore alternative models of the syntax-semantics interface. In particular, we will explore

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relational models where several distinct semantic representations can correspond to the same syntactic
structure.

2.3 Discourse Dynamics

It is well known that the interpretation of a discourse is a dynamic process. Take a sentence occurring
in a discourse. On the one hand, it must be interpreted according to its context. On the other hand, its
interpretation affects this context, and must therefore result in an updating of the current context. For
this reason, discourse interpretation is traditionally considered to belong to pragmatics. The cut between
pragmatics and semantics, however, is not that clear.

As we mentioned above, we intend to apply to some aspects of pragmatics (mainly, discourse dynam-
ics) the same methodological tools Montague applied to semantics. The challenge here is to obtain a
completely compositional theory of discourse interpretation, by respecting Montague’s homomorphism
requirement. We think that this is possible by using techniques coming from programming language
theory, in particular, continuation semantics, and the related theories of functional control operators.

We have indeed successfully applied such techniques in order to model the way quantifiers in natural
languages may dynamically extend their scope [55]. We intend to tackle, in a similar way, other dynamic
phenomena (typically, anaphora and referential expressions, presupposition, modal subordination...).

What characterizes these different dynamic phenomena is that their interpretations need information
to be retrieved from a current context. This raises the question of the modeling of the context itself.
At a foundational level, we have to answer questions such as the following. What is the nature of the
information to be stored in the context? What are the processes that allow implicit information to be
inferred from the context? What are the primitives that allow a context to be updated? How does the
structure of the discourse and the discourse relations affect the structure of the context? These questions
also raise implementation issues. What are the appropriate data types? How can we keep the complexity
of the inference algorithms sufficiently low?

2.4 Common Basic Resources

Even if our research primarily focuses on semantics and pragmatics, we nevertheless need syntax. More
precisely, we need syntactic trees to start with. We consequently need grammars, lexicons, and parsing
algorithms to produce such trees. During the last years, we have developed the notion of interaction
grammar [58] and graph rewriting [3, 4] as models of natural language syntax. This includes the develop-
ment of grammars for French [67], together with morphosyntactic lexicons. We intend to continue this
line of research and development. In particular, we want to increase the coverage of our grammars for
French, and provide our parsers with more robust algorithms.

Further primary resources are needed in order to put at work a computational semantic analysis
of utterances and discourses. As we want our approach to be as compositional as possible, we must
develop lexicons annotated with semantic information. This opens the quite wide research area of lexical
semantics.

Finally, when dealing with logical representations of utterance interpretations, the need for inference
facilities is ubiquitous. Inference is needed in the course of the interpretation process, but also to exploit
the result of the interpretation. Indeed, an advantage of using formal logic for semantic representations is
the possibility of using logical inference to derive new information. From a computational point of view,
however, logical inference may be highly complex. Consequently, we need to investigate which logical
fragments can be used efficiently for natural language oriented inference.

3 Research program

3.1 Overview

The research program of Sémagramme aims to develop models based on well-established mathematics.
We seek two main advantages from this approach. On the one hand, by relying on mature theories, we
have at our disposal sets of mathematical tools that we can use to study our models. On the other hand,

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developing various models on a common mathematical background will make them easier to integrate,
and will ease the search for unifying principles.

The main mathematical domains on which we rely are formal language theory, symbolic logic, and

type theory.

3.2 Formal Language Theory

Formal language theory studies the purely syntactic and combinatorial aspects of languages, seen as
sets of strings (or possibly trees or graphs). Formal language theory has been especially fruitful for the
development of parsing algorithms for context-free languages. We use it, in a similar way, to develop
parsing algorithms for formalisms that go beyond context-freeness. Language theory also appears to be
very useful in formally studying the expressive power and the complexity of the models we develop.

3.3 Symbolic Logic

Symbolic logic (and, more particularly, proof theory) is concerned with the study of the expressive and
deductive power of formal systems. In a rule-based approach to computational linguistics, the use of
symbolic logic is ubiquitous. As we previously said, at the level of syntax, several kinds of grammars
(generative, categorial. . . ) may be seen as basic deductive systems. At the level of semantics, the meaning
of an utterance is captured by computing (intermediate) semantic representations that are expressed as
logical forms. Finally, using symbolic logics allows one to formalize notions of inference and entailment
that are needed at the level of pragmatics.

3.4 Type Theory and Typed Lambda-Calculus

Among the various possible logics that may be used, Church’s simply typed λ-calculus and simple theory
of types (also known as higher-order logic) play a central part. On the one hand, Montague semantics
is based on the simply typed λ-calculus, and so is our syntax-semantics interface model. On the other
hand, as shown by Gallin, the target logic used by Montague for expressing meanings (i.e., his intensional
logic) is essentially a variant of higher-order logic featuring three atomic types (the third atomic type
standing for the set of possible worlds).

4 Application domains

4.1 Deep Semantic Analysis

Our applicative domains concern natural language processing applications that rely on a deep semantic
analysis. For instance, one may cite the following ones:

• textual entailment and inference,

• dialogue systems,

• semantic-oriented query systems,

• content analysis of unstructured documents,

• (semi) automatic knowledge acquisition,

• discourse structure analysis (argumentative relations, discourse markers),

• lexical resources.

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4.2 Text Transformation

Text transformation is an application domain featuring two important sub-fields of computational
linguistics:

• parsing, from surface form to abstract representation,

• generation, from abstract representation to surface form.

Text simplification or automatic summarization belong to that domain.

We aim at using the framework of Abstract Categorial Grammars we develop to this end. It is indeed a
reversible framework that allows both parsing and generation. Its underlying mathematical structure of
λ-calculus makes it fit with our type-theoretic approach to discourse dynamics modeling.

4.3 Types for discourse markers

While there is a rich descriptive literature on Discourse Markers (DM), for instance words/expressions
like so or yet in English, the question of their representation in type systems is understudied. In addition
to basic types such as individuals or events, or simple functional types (properties, etc.), DM are known
to operate on domains like states of affairs, beliefs or speech acts. The entities inhabiting these domains
are themselves complex. For instance, speech acts involve discourse planning in the form of a network of
intentions and actions. Moreover, DM can combine with one another, forming clusters whose meaning is
not always apparent from the meanings of the component DM. Within the context of the ANR CODIM,
we aim at developing a typing system for (i) taking into account the array of types denoted by DM and (ii)
addressing the questions of the semantic nature of their combinations.

5 Social and environmental responsibility

5.1 Footprint of research activities

ANR InExtenso: WP4 of the project is dedicated to the evaluation of the environmental impact of the
LLMs. More precisely, it aims at proposing a method for measuring the environmental impact of digital
health and use it in the project evaluations and beyond.

6 Highlights of the year

Sémagramme organized the 15th International Conference of Computational Semantics (IWCS), the
annual conference of the special interest group on computational semantics of the ACL, Nancy 20-23
June 2023.

7 New software, platforms, open data

7.1 New software

7.1.1 ACGtk

Name: Abstract Categorial Grammar Development Toolkit

Keywords: Natural language processing, NLP, Syntactic analysis, Semantics

Scientific Description: Abstract Categorial Grammars (ACG) are a grammatical formalism in which
grammars are based on typed lambda-calculus. A grammar generates two languages: the abstract
language (the language of parse structures), and the object language (the language of the surface
forms, e.g., strings, or higher-order logical formulas), which is the realization of the abstract
language.

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ACGtk provides two software tools to develop and to use ACGs: acgc, which is a grammar compiler,
and acg, which is an interpreter of a command language that allows one, in particular, to parse and
realize terms.

Functional Description: ACGtk provides a piece of software for developing and using Abstract Categorial

Grammars (ACG).

Release Contributions: New version of the software that provides a new command language and new
functionalities for the interpreter (completion, sorting of parsing structures, use of magic set
rewriting technic for parsing). The compiler now accepts UTF-8, and a new syntax for identifiers,
binders and operators was defined.

URL: https://gitlab.inria.fr/ACG/dev/ACGtk

Publications: hal-01242154, hal-01328702, tel-01412765, inria-00112956, inria-00100529

Contact: Sylvain Pogodalla

Participants: Philippe De Groote, Pierre Ludmann, Jiri Marsik, Sylvain Pogodalla, Vincent Tourneur

7.1.2 Grew

Name: Graph Rewriting

Keywords: Semantics, Syntactic analysis, NLP, Graph rewriting

Functional Description: Grew is a Graph Rewriting tool dedicated to applications in NLP. Grew takes
into account confluent and non-confluent graph rewriting and it includes several mechanisms that
help to use graph rewriting in the context of NLP applications (built-in notion of feature structures,
parametrization of rules with lexical information).

News of the Year: In 2023, 4 new versions were released. The main new features are: non-injective
matching, a new "with" keyword for positive filtering, a syntax extension to express disjunction on
node matching.

URL: https://grew.fr/

Publications: hal-03724068, hal-03177701, hal-01930591, hal-01814386, hal-03021720, hal-04387830,

hal-04387852, hal-03724068, hal-03724129, hal-03846825

Contact: Bruno Guillaume

Participants: Bruno Guillaume, Guy Perrier, Guillaume Bonfante

7.1.3 SLODiM

Name: SLODiM

Keywords: NLP, Discourse, Dialogue, French

Functional Description: SLODiM is a software package for the analysis of oral French. It is more partic-
ularly developed to allow the analysis of interviews with clinicians in order to identify language
behaviours characteristic of mental pathologies.

Release Contributions: The latest version integrates new treatments in particular at the level of the

identification of the backchannels.

A version without the graphical representations is available without an account. Its purpose is to
make visible the treatments that are produced in the system.

News of the Year: Implementation of new analyses, facilitation of automatic use on various corpora of

spontaneous speech.

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URL: https://academics.slodim.loria.fr/

Contact: Maxime Amblard

Partners: Loria, Université de Lorraine, CNRS

7.1.4 HostoMytho

Keywords: Semantic annotation, Annotation tool, Semantic, Medical applications, NLP

Functional Description: HostoMytho is a GWAP, or "game with a purpose" developed within the frame-
work of the CODEINE ANR project. The aim of the game is to allow users to annotate medical
files generated automatically, in order to evaluate their plausibility (quality of the language and
medical semantics) and to add different layers of information (negation, hypothesis, time, etc.).
HostoMytho is multiplatform.

Contact: Bertrand Remy

Partners: LISN, CEA-List

7.1.5 Arborator-Grew

Name: Arborator’s Collaborative Annotation

Keywords: Annotation tool, Syntactic analysis

Functional Description: The online interface allows managing collaborative annotation projects in
dependency syntax. It is possible to use Grew queries and also to directly rewrite graphs in the
annotation tool.

News of the Year: Different features have been developed during 2023: redefinition of different types of
user roles and access, integration of a labeling system in order to organize the annotation workflow,
implementation of a full synchronization between Github and ArboratorGrew, improvement of
different existing features (such as Lexicon, Grew search and Grew rule edition), enhancement of
the parser UI, fixes for bugs and issues reported by the users, development of user documentation.

URL: https://arborator.github.io/

Publication: hal-03021720

Contact: Bruno Guillaume

Participants: Khensa Amani Daoudi, Bruno Guillaume, Gael Guibon, Kim Gerdes, Kirian Guiller

Partners: Université Paris Nanterre, LIMSI, LISN

7.2 Open data

7.2.1 Morphosyntactic Treebanks

Participants:

Bruno Guillaume, Valentin Richard, Guy Perrier.

Sémagramme actively participates in the maintenance and the development of morphosyntactic
treebanks in the Surface Syntactic Universal Dependencies (SUD) and Universal Dependencies (UD)
projects.

Several French treebanks are involved:

• SUD_French-GSD

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• SUD_French-ParisStories

• SUD_French-Rhapsodie

• SUD_French-Sequoia

A treebank of French sentences containing interrogative clauses, called French Interrogative Bank
(UD_French-FIB) was extracted from French UD treebanks using the program FUDIA, designed for this
purpose.

In 2023, by collaborating with linguists and field linguists in the Autogramm project, Sémagramme
is also helping to develop treebanks for new languages (Haitian Creole and Hausa) and to improve and
extend existing treebanks (Beja, Naija and Zaar).

• In Haitian Creole: SUD_Haitian_Creole-Autogramm

• In Hausa: SUD_Hausa-Autogramm

• In Beja: SUD_Beja-NSC

• In Naija: SUD_Naija-NSC

• In Zaar: SUD_Zaar-Autogramm

8 New results

8.1 Syntax-Semantics Interface

Participants: Maxime Amblard, Marie Cousin, Philippe de Groote, Bruno Guil-
laume, Maxime Guillaume, Pierre Ludmann, Sylvain Pogodalla, Siy-
ana Pavlova, Valentin Richard, Priyansh Trivedi.

8.1.1 Abstract Categorial Grammars

Feature Structure ACG has proven to be a powerful framework with well-defined theoretical properties.
It was, however, lacking a facility which is useful and widely used for grammar engineering: feature
structures. The latter are often used to express in a concise way some combinatorial properties related to
morphosyntactic properties of expressions, for instance subject-verb agreement.

We worked on extending the ACG type system to provide a generic feature structure framework. This
extension relies on a restricted addition of the product (records) and dependent types. We also considered
the reduction of grammars using this extension to Datalog programs (which is used to implement ACG
parsing in ACGtk, see Sec. 7).

An experiment with the actual Yseop proprietary grammars and ACG system with features was run
and resulted in a significant improvement, both in the size of the grammar (decrease) and the efficiency
of text generation (increase) [38].

Multityped ACG (mACG) and Weighted ACG Symbolic parsing with large coverage grammars usually
leads to combinatorial explosion of syntactic ambiguities (a single expression has many syntactic ana-
lyses). A widespread method to tackle this issue is to use statistics and probabilities, leading for instance
to probabilistic Context Free Grammars (pCFGs) and probabilistic Tree Adjoining Grammars (pTAGs). An
important goal is then to also extend ACGs with probabilities or weights.

Yet, ACGs come with features that make this extension non-trivial. In particular, ACGs can be com-
posed by making the parse structures of a grammar the surface structures of another ACG. The resulting
composition is a full-flavored ACG. Because adding weights to ACGs naturally leads to refining admiss-
ible abstract structures (associated with a weight), ACG composition does not anymore correspond to
functional composition. We introduced multityped ACGs (mACGs) [61] to support weighting extension,
showing that a suitable notion of composition can be defined for multityped ACGs as well. We extended

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this work also by introducing weighted ACG and showing that composition still holds and that weighted
ACGs can encode hidden Markov models (not yet published).

Encoding of Meaning-Text Theory Into ACGs Meaning-Text Theory (MTT) is a linguistic theory geared
towards generating natural language expressions from semantic representations [62]. It relies on seven
representation levels (e.g., semantics, deep syntax, surface syntax, etc.). Structures at each level are
related to structures at the adjacent levels by rewriting devices. MTT use the key concept of paraphrase,
especially in these rewriting devices. ACGs come with several composition modes, one of which in
particular corresponds to transduction of (tree or graph) structures. We used this ability to study the
extent to which MTT architecture as well as the paraphrase operation can be encoded into ACGs. We
showed that, while some of the MTT mechanisms as well as some linguistic phenomena can be faithfully
accounted for within ACGs, some other ones require additional mechanisms [28, 16].

8.1.2 Formal semantics of dependency relations

We have laid the foundations of a formal semantic theory for dependency grammars [42] by exploiting
the following principles:

1. Dependency relations are represented as binary functions of type α → β → α (or β → α → α), where

α is the syntactic category of the governor and β is the syntactic category of the governee.

2. By virtue of a coherence principle, the different ways of encoding a dependency structure by means

of a λ-term should all give rise to the same semantic interpretation.

3. The semantic interpretation of a syntactic category CAT is a type of the form β1 → · · · βn → α, where
α is the Montagovian interpretation of CAT and β1, . . . βn are the Montagovian interpretations of
the syntactic categories of the phrases whose heads can potentially be governed by the head of a
phrase of category CAT.

4. Saturating operators allow phrases to recover their usual Montagovian interpretations.

5. Verbs and verbal phrases are semantically interpreted as sets of sets of events.

The resulting theory is fully compositional, allows for a treatment of quantification, and is robust in the
sense that it supports the interpretation of partial dependency structures. We have then demonstrated
that our theory allows one to deal with more advanced syntactic phenomena [37]. We considered two
cases: the relative clauses (which depend on the acl:relcl dependency relation) and the open clausal
complements (which depend on the xcomp dependency relation).

8.1.3 Lexical Semantics and Linguistic Knowledge

Discourse Markers are lexical items which, like so or well in English, are used to organize discourse
or to manifest epistemic or affective states of the speaker. Their semantic contribution is difficult to
characterize in a sufficiently general way, in particular because they denote entities with a rich semantic
structure, such as beliefs or speech acts. Preliminary results suggest that it is necessary to view their
semantic update potential as involving intentions, actions and, generally speaking, dynamic modalities.
The question then arises of what formal languages/systems are the most appropriate to capture these
features (modal logic? Belief-Desire-Intention systems, interactive semantics à la Ginzburg, monads to
mimic side effects, etc.).

8.1.4 Semantic Representation

We have been studying and comparing different existing semantic representation formalisms (Abstract
Meaning Representation (AMR), Discourse Representation Theory (DRT), Uniform Meaning Represent-
ation (UMR), to name but a few). The aim is to determine, through theoretical and empirical studies,
whether these formalisms are compatible and if they encode the same level of semantic information.
This gives us a better understanding to open a discussion on what we want semantic representation to be,
and build the groundwork for a unifying formalism.

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Inria Annual Report 2023

In [24], an empirical comparison of two of the most popular semantic representation formalisms,
AMR and DRS, is presented. Its goal is to explore the viability of transforming graphs from one framework
into another to construct parallel datasets, but also to obtain an understanding of the similarities and
differences through the use of annotated data. As no freely available parallel datasets that are common
between the two exist, a sample of 200 sentences already annotated in DRS was taken and also annotated
in AMR. A corpus-based approach was then used to build a graph rewriting system from DRS to AMR.
This gives insights into where the two formalisms differ in encoding various semantic phenomena.

In [25], a set of structural (encoding of negation, temporal information, modality, etc.) and global
(universality, scalability, etc.) features to consider when designing formalisms, and against which formal-
isms can be assessed, is proposed. A comparison of eight semantic representation formalisms, some that
are graph-based and some that are logic-based, across the proposed features is provided, complemented
by a comparison across the features of a more entailment-oriented corpus (FraCaS).

In [32], a first version of a layered approach to semantic representation is presented. It is motivated
by the need for a semantic representation formalism which can encode a rich variety of semantic
phenomena, while remaining simple to annotate and easy to read. The representation derives its
core predicate-argument structure from Abstract Meaning Representation (AMR). The structure is then
extended to encode "features", where each feature is considered as a layer representing a semantic
phenomenon that is to be encoded. This approach allows for the possibility to exclude layers from the
representation easily, while still being able to represent phenomena such as scope that are difficult for
most graph-oriented formalisms.

8.1.5 Semantics of questions

Natural language statements are composed not only of declarative sentences but also of interrogative
ones. Moreover, sentences cannot be categorized into purely declarative or purely interrogative sentences.
Typically, a declarative statement may contain an indirect interrogative clause:

(a) I don’t know where Mary is.

Conversely, a direct interrogative clause may contain a declarative subordinate:

(b) Do you know that Mary is here?

This interaction between declarative and interrogative clauses is particularly present in dialogues,

where the logical notion of answerhood is as significant as the one of inference.

In order to tackle this issue from a formal standpoint, we investigated the properties and possible
uses of inquisitive semantics, which is a formal semantic theory based on a logic that provides a uniform
treatment of both declarative and interrogative expressions. Valentin Richard is currently working on
a semantic model for the presuppositions and dynamic referential effects of interrogative pronouns.
Some further investigation is required to understand the interaction between these presuppositions and
possibility modals.

8.2 Discourse Dynamics

Participants: Maxime Amblard, Philippe de Groote, Jacques Jayez, Chuyuan Li,

Michel Musiol.

8.2.1 Dialogue Modeling

Together with Chloé Braud (IRIT), Maxime Amblard and Chuyuan Li continued to work on modeling
discourse. Discourse processing suffers from data sparsity, especially for dialogues. As a result, they
have explored approaches to build discourse structures for dialogues, based on attention matrices from
pretrained language models (PLMs). They have investigated multiple tasks for fine-tuning and show that
the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information
in PLMs, they have proposed an unsupervised and a semi-supervised method. The proposals thereby

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achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and
semisupervised methods, respectively. When restricted to projective trees, the scores improved to 63.3
and 68.1, see [23].

They also start working on the design of a full discourse parser [22]. Discourse analysis plays a crucial
role in Natural Language Processing (NLP) and has demonstrated its usefulness in various downstream
applications like summarization and question answering. In this work, they studied discourse in dia-
logues: an under-explored setting due to significant data scarcity challenge. They conducted discourse
parsing within a pipeline: first predicting the discourse structure, and then identifying the relations within
the structure. Using only 50 examples as gold training data, the methods achieve competitive results
compared to supervised state-of-the-art in-domain and much stronger performance cross-domain, with
also better stability.

Maxime Amblard and Amandine Decker continue to work on topic shift modeling [17]. Topics
play an important role in dialogue coherence, as what is currently discussed constrains the possible
contributions of the participants, and initiating a topic while the previous one is still under discussion
may be confusing without appropriate signals. However, the way to actually define the notion of topic is
debated in linguistics and not sufficiently discussed in dialogue modeling. A precise description of topics
and topic shifts in conversation would contribute to a better understanding of what makes us judge a
sequence of utterances to be coherent. In order to analyze different types of topic shifts, they proposed to
create a corpus of written task-oriented conversations (discussion of the ethical dilemma of the balloon
task), where the dialogues happen by message exchanges. Such a controlled setting where the main topic
is fixed, and subtopics are more easily identifiable, could be very helpful when it comes to understanding
how people change the topic and react to topic shifts in dialogues.

8.2.2 Discourse Markers

Jacques Jayez is currently working with Mathilde Dargnat (ATILF), Paola Herreño (Ph. D. candidate
ATILF-LLF) and Maeva Sillaire (Ph. D. candidate ATILF) on the semantic representation of D(iscourse)
M(arkers). DM are words/expressions like so or well in English which help structuring discourse or
communicating speakers’ internal epistemic or affective states. The domain-based approach initiated
in the 90s consists in defining different types (aka domains) of semantic objects, like states of affairs,
beliefs or speech acts. Such objects have a rich internal structure, which calls for a sufficiently expressive
representation. For instance, speech acts involve discourse planning. Moreover DM can impact various
layers of meaning (propositional content, presupposed content, etc.). Our goal is to implement formally
the domain intuition by using tools from languages with a flexible subtyping mechanism (Ocaml) and
to investigate whether the notion of monad, familiar in Haskell, can help us to characterize some ‘side
effects’ of DM.

Jacques Jayez is also working on the argumentative dimension of discourse, using a combination of

standard Bayesian approach and game-theoretical notions [48, 39].

8.2.3 Pathological Discourse Modeling

Michel Musiol is once again part-time delegate at Inria (délégation nationale SHS) for the period 2023-
2024. This proximity has enabled us to pursue an active collaboration on the modeling of pathological
and clinical discourse.

In the context of the MePheSTO project (Digital Phenotyping for Psychiatric Disorders from Social
Interaction - DFKI-Inria AI project), a multimodal perspective on this issue has been introduced. This
includes the development of an interlocutory model of what might be termed a "therapeutic effect" in
clinical/psychopathological interviewing, with regard to the cognitive-discursive profile and oculomotor
behavior of participants (Amandine Lecomte ´s thesis forthcoming 2024).

Indeed, based on a previous study addressing the issue of cognitive impairment bypass based on
the dynamics of the repetition process [59] and another modeling visual attention in the psychologist-
schizophrenic patient interaction [66] as well as on the basis of new data (for example, from subjects with
ultra-high risk of developing psychosis or subjects who have experienced a major depressive episode [12],
in the MePheSTO project), Maxime Amblard, Michel Musiol and Amandine Lecomte have explored the

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dependent relationships between supportive visual behavior and interaction dynamics, on the one hand,
and the relationships between mental pathology and oculomotor disorders, on the other hand.

In addition, in [65] they have sketched a methodology for analyzing speech disorders, which will have
the particularity of helping to select the discontinuous sequences most likely to carry thought disorders.
They have anticipated the development of a modeling system based on the principles of pragmatic
linguistics and formal semantics, which, when applied to carefully selected discontinuous discourse
sequences [63], will have a good chance of revealing the nature of the underlying thought disorders. They
compared the conjectures with the results of an earlier study on the discovery of four "proven" types of
discontinuous sequences, and showed which of these sequences can therefore be considered carrying
thought disorders.

They also analyzed these sequences by testing certain principles of semantic modeling in order to
identify the nature of the disorders and thought operations underlying the discontinuous sequences
concerned [64, 65]. They show that discursive thought disorders should not be considered simply as
the expression of a dysexecutive syndrome, but also as a device likely to affect more complex thought
operations such as inferences involved in the conversational context representation system, in semantic
memory and in calculating the meaning of utterances or in calculating the meaning of the speaker.

Improving the heuristics of formal systems for recognizing speech disorders and interpreting thought
disorders on the basis of more appropriate and accurate semantic modeling may lead to the develop-
ment of more discriminating and effective diagnostic tools. The formatting of the formal systems they
have achieved will make it possible to represent the interlocutory structure of the disorder more and
more accurately in its natural context of expression (speech), and should lead to the development of
computerized diagnostic aids.

Finally, the increased precision of formal modeling applied to communication disorders should
also make it possible to test the hypothesis that certain discourse configurations are related to thought
disorders (in the broad sense), while others reveal cognitive dysfunctions that have more to do with the
conditions of the possibility of discourse.

In this line, Michel Musiol has also updated MOS-SF36 norms in the young French population [68].
This program focuses on accurate and rapid diagnosis, as well as long-term therapeutic follow-
up. These are major challenges for contemporary psychopathology. Thanks to modeling [59, 60] and
computing [54], this work is developing multimodal methodologies for investigating symptoms (language,
speech, neuropsychological and cognitive processes, eye movements and visual attention) that are
sufficiently accurate to give rise in the medium term to the development of computerized diagnostic and
therapeutic follow-up tools for the benefit of those involved in mental health.

8.2.4 Cognitive traces of side issues

It is by now widely believed that natural language communication operates at several levels. This means
that information is distributed across several partially independent dimensions. For instance, a sentence
like My stupid neighbor made noise again, simultaneously conveys that my neighbor made noise (the
truth-conditional content), that the speaker considers he is stupid (an expressive, side issue 1) and that
he had made noise before (the presupposition of again, side issue 2). While these phenomena have
been extensively described from an empirical perspective, there is at present no unified framework for
representing their differences and possible interactions under a formal, computational or cognitive point
of view.

We examined the motor effects of presuppositions, using the convenient lexical material of factive
verbs, that is, verbs which presuppose the truth of the complement clause. For example, Mary knows
that Paul cheated on the exam presupposes that Paul cheated on the exam (side issue) and asserts (truth-
conditional content) that she believes that. It has been shown that the oral presentation of movement-
related verbs like jump or push elicits some activation in the motor cortex and finally results into an
involuntary contraction of the thumb-index arc, which can be recorded by a special electromagnetic cell,
called a grip force sensor.

We adapted this technique to the case of factive verbs on a series of sentences of the form Mary
knows that Paul throws the ball, compared with high base-level sentences like Paul throws the ball and
low base-level sentences like Paul does not throw the ball. Summarizing, our results indicate that the
sentences with the factive verbs elicit a very similar response to that of high base-level ones, and, as

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expected, a different response from that of low base-level one. This suggests that, at least for factive verbs,
the presupposed status leaves no trace of a special cognitive treatment, which would lead for instance, to
a delayed or weaker motor response.

However, when applied to more complex negative sentences like Mary does not know that Paul throws
the ball, there is no evidence of a motor trace. This is in agreement with observations in the literature
suggesting that, under negation, presuppositions are more ’difficult’ to process than in simple assertive
sentences. More precisely, in the case of motor response, negation interacts with the presupposition,
which suggests that truth-conditional content and side issue cognitive processing cannot be totally
separated [8].

8.3 Common Basic Resources

Participants: Maxime Amblard, Hee-Soo Choi, Philippe de Groote, Bruno Guillaume,

Guy Perrier, Sylvain Pogodalla, Karën Fort, Valentin Richard.

8.3.1 Universal Dependencies and Surface Syntactic Universal Dependencies

The Universal Dependencies project (UD) aims at building a syntactic dependency scheme which allows
for similar analyses for several different languages. Bruno Guillaume and Guy Perrier are active in the
UD community, and participate in the development and the improvement of the French data in this
international initiative. In 2023, two new versions (2.12 and 2.13) of the UD data were released.

During 2023, they continued working, in collaboration with Sylvain Kahane, Kim Gerdes and their
teams on the promotion of the Surface Syntactic Universal Dependencies (SUD) framework. SUD is
an annotation scheme for syntactic dependency treebanks, that is almost isomorphic to UD (Universal
Dependencies). Contrary to UD, it is based on syntactic criteria (favoring functional heads) and the
relations are defined on distributional and functional bases. In [41], they describe how function words
are considered in the SUD formalism and compare with other approaches.

A suggestion to add clause type features and change the annotations of some French expressions

regarding interrogatives was investigated by Valentin Richard [33].

8.3.2 Multiword annotation in the Parseme project

In [40], the new version 1.3 of the PARSEME multilingual corpus annotated with verbal multiword
expressions is presented. Since the previous version, new languages have joined the undertaking of
creating such a resource, some of the already existing corpora have been enriched with new annotated
texts, while others have been enhanced in various ways. The PARSEME multilingual corpus represents
26 languages now. All monolingual corpora therein use Universal Dependencies v.2 tagset. They are
(re-)split observing the PARSEME v.1.2 standard, which puts an impact on unseen VMWEs. With the
current iteration, the corpus release process has been detached from shared tasks; instead, a process for
continuous improvement and systematic releases has been introduced.

During the MWE workshop, we showed [20] how Grew and Grew-match can be adapted to MWE
annotated data of the Parseme project. The tool can be used for linguistic exploration on the data, for
helping the manual annotation process and to search for errors or inconsistencies in the annotations.

8.3.3 Induction of Descriptive Grammars

The ANR project Autogramm (Induction of descriptive grammar from annotated corpora) started in
January 2022. The goal of this project is to automate, as far as possible, the extraction of descriptive
grammars and grammatical descriptions from annotated corpora for linguistic and typological studies.
The project also promotes the development of treebanks for low-resourced languages, in order to extract
quantitative descriptive grammars for these languages.

In [31], the project was presented to the French community. Treebanks for new languages were also

released during 2023, one in Zaar and one in Haitian Creole.

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8.3.4 Mapping Lexical Resources

Lexical resources are essential for the development of tools and methods for the various tasks of NLP.
In French, the existing resources are heterogeneous in their size, their construction and their level of
linguistic description, which opens the way to explore a method to group or link/map them automatically.
French lexical resources are plentiful, but have not all been used in the same way. In [27], Hee-Soo Choi
she presented a diachronic analysis of 34 French lexical resources over a 20-year period in the proceedings
of the TALN conference, showing differences in the types of application and reuse of resources by the
community.

8.3.5 Sentence Semantic Similarity Corpus

In the context of the CODEINE ANR project and more specifically of Nicolas Hiebel’s PhD thesis, Karën
Fort worked with Aurélie Névéol (LISN-CNRS) and Olivier Ferret (CEA) on the creation of a sentence
semantic similarity corpus for French on the clinical domain. A paper on the subject was presented at
EACL 2023 [21], a rank A conference of the domain. An adaptation in French of the paper was accepted at
TALN 2023 [29].

The key idea of the project is to use confidential corpora to automatically generate anonymous
synthetic texts capable of emulating real documents from the perspective of their linguistic characteristics.
The project will rely on a Games With A Purpose and crowd sourcing to validate and then annotate these
synthesized clinical texts.

This game, developed by Bertrand Remy, is called HostoMytho (see Section 7.1.4), and includes various
mini-games for different annotation layers, such as negation, error typing, or plausibility rating. The
game is multi-platform, and therefore intended to be used on the web, on Android and iOS.

8.4 Ethics and biases

Participants: Karën Fort, Maxime Amblard, Michel Musiol, Marc Anderson,

Fanny Ducel.

8.4.1 Ethics@Loria

Karën Fort originated a working group at LORIA for AI ethics (ethics@loria), involving researchers from
various teams, including Maxime Amblard, Marc Anderson, Armelle Brun (BIRD), Mathieu d’Aquin
(Orpailleur), Christophe Cerisara (Synalp), Anne Bonneau (Multispeech), Slim Ouni (Multispeech) and
Abdessamad Imine (Pesto). Aurore Coince helped manage the group. Ethics@loria proposed the Doctoral
training on Ethics "Write your dystopia".

8.4.2 Evaluating Stereotypes in Masked Language Models in Many Languages

Following the creation of French CrowsPairs [6], Karën Fort contacted researchers interested in creating
a CrowsPairs corpus for their language, in order to test the language models. The group got bigger and
bigger to finally include 22 researchers (including Fanny Ducel, from the team) for 7 languages (German,
Maltese, Spanish, Italian, Chinese, standard Arabic and Catalan). The group worked together for more
than a year to produce corpora and test masked language models in these 7 new languages. The corpora
are freely available, along with the code to test the language models and the guidelines we followed for
the adaptation.1 It’s important to notice that this work has been performed without any funding. The
work has been detailed in a paper submitted at LREC-COLING 2024.

8.4.3 Evaluating stereotypes in autoregressive language models

Fanny Ducel authored a critical literature review on the topic of stereotypical biases in language models,
under the supervision of Karën Fort and Aurélie Névéol. She presented this work at the Workshop on

1The GitLab repository will be made public upon acceptance of the publication

Project SEMAGRAMME

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Algorithmic Injustice in Amsterdam in June 2023 [36]. This study was part of her internship and master’s
thesis on the evaluation of stereotypical biases in autoregressive language models, that has become her
PhD topic since October 2023.

Karën Fort is PI of a new 4 year ANR project (2023-2027), InExtenso (Intrinsic and Extrinsic evaluation
of biases in large language models), in collaboration with Rouen’s hospital (CHU) and LISN-CNRS. The
project aims at better identifying stereotyped biases in LLMs in French and, when possible, mitigate
them. At LORIA, the project reaches beyond Sémagramme, as Miguel Couceiro (Orpailleur) joined us on
the bias mitigation part.

8.4.4 NLP for NLP and Ethics

Karën Fort originated a research group on the impact of the BigTech companies on NLP (industry
presence, potential thematic shifts, participation in paper authorship). This group is composed of Aurélie
Névéol (LISN), Saif Mohammad (National Research Council Canada), Mohamed Abdalla (University of
Toronto), Terry Lima Ruas and Jan Philip Wahle (Wuppertal University) and Fanny Ducel (M2 student at
Sorbonne University). The group regularly gathered from September to December 2022 to perform both
an automatic study on all the ACL Anthology papers and a manual one from the ACL 2022 papers. The
resulting paper was accepted at ACL 2023 [15], the A* conference in NLP.

8.4.5 Ethics in AI Integration into Industry

Karën Fort and Marc Anderson completed the Ethics by Design component of the EU Horizons AI-
Proficient project, which ended in October 2023. The final year of the project involved a review of ethics
integration in the project in comparison to the EU High Level Expert Group Guidelines, as well as an
analysis of the results of the implementation of ethical recommendations given to project partners. That
analysis was aimed at providing a model for future projects and AI ethics researchers for operationalizing
AI ethics, which remains very rare in the domain. Contributions were made to various late-stage deliver-
ables of the project, namely those of the WP6 which dealt with project validation, and in particular the
ethics team provided the AI-Proficient Deliverable 6.4: AI-Proficient Ethical Recommendations.

Marc Anderson [10] authored a journal paper in Studia Philosophica Wratislaviensia carrying out
an analysis of public ethical concerns and expectations for AI as compared to the older technology
of the telegraph. The latter paper was initially presented as “AI Ethics and the Lessons of History,” in
the 2nd International Conference on the Ethics of Artificial Intelligence (2ICEAI)[35]. Marc Anderson
also presented at the Sohoma 2023 conference [43], on the topic of ethical sustainability in digital
manufacturing and industry 4.0 generally.

The ongoing collaboration between Karën Fort and Marc Anderson resulted in no new common
article in 2023 (one has been accepted and will be published in 2024, though), but Karën Fort has been
invited as a keynote at ERGO IA [13] to talk about how the recommendations made by the ethics group
were taken into account in the AI-Proficient project.

9 Bilateral contracts and grants with industry

9.1 Bilateral Grants with Industry

9.1.1 Yseop

Participants:

Philippe de Groote, Maxime Guillaume, Sylvain Pogodalla.

The Sémagramme team has set up a Cifre thesis contract with Yseop on ACG extensions and use in an
industrial environment.

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Inria Annual Report 2023

10 Partnerships and cooperations

10.1 International research visitors

10.1.1 Visits to international teams

Research stays abroad

Amandine Decker

Visited institution: University of Gothenburg.

Country: Sweden.

Dates: 25 March to 14 September.

Context of the visit: Amandine Decker is working on her doctoral thesis under the joint supervision of

Maxime Amblard and Ellen Breitholtz (University of Gothenburg).

Mobility program/type of mobility: European Erasmus cooperation.

10.2 European initiatives

10.2.1 H2020 projects

AI-Proficient

Participants: Marc Anderson, Karën Fort.

Title: Artificial intelligence for improved production efficiency, quality and maintenance

Duration: November 2020–October 2023

Coordinator: Benoit Iung (CRAN, Université de Lorraine)

Partners: Université de Lorraine (coordination), Continental (industrial partner), Ineos (industrial part-
ner), Institute Mihailo Pupin (Serbia), Tekniker (Spain), Ibermatica (Spain), TenForce (Belgium),
VTT (Finland), Inos Hellas (Greece), ATC Athens Technology Center (Greece)

Participants: Marc Anderson, Karën Fort

Abstract: AI-Proficient carries out research on integrating AI services into the industrial contexts of three
factories located in France, Belgium, and Germany. Two Sémagramme members make up the
ethics team for the project since its beginning in 2020: Karën Fort (project ethics officer) and Marc
Anderson (postdoctoral fellow).

10.2.2 Other european programs/initiatives

• Bruno Guillaume is a member of the core group of the cost action: CA21167 - Universality, diversity
and idiosyncrasy in language technology (UniDive). He is the leader of the working group named
"Corpus Annotation".

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10.3 National initiatives

10.3.1 ANR Project: InExtenso

Participants: Karën Fort, Maxime Amblard, Michel Musiol, Fanny Ducel.

Title: Intrinsic and Extrinsic evaluation of biases in large language models

Duration: October 2023–September 2027

Coordinator: Karën Fort

Partners: CHU Rouen, LISN, LORIA

Participants: Maxime Amblard, Fanny Ducel, Karën Fort (coordinator), Michel Musiol, Miguel Couceiro

Abstract: Large Language Models (LLM) are the Swiss Army knife of today’s Natural Language Processing
(NLP). They often outperform the state-of-the-art on benchmarks commonly used in the field
for tasks such as part-of-speech tagging, text classification and named-entity recognition, thus
paving the way to a myriad of end-user applications. However, it has been shown that LLM exhibit
major ethical issues including significant environmental impact, mirroring and amplification of
stereotyped biases, which in turn have a disproportionate impact on historically disadvantaged
social groups. It is urgent to address the social impact of NLP as the applications we develop, such
as chatGPT, are now directly made available to end users. The detection and mitigation of biases
have therefore become an active area of research in the past few years, focusing mainly on Masked
Language Models (MLM) such as BERT in English and the North American social context. Several
sources of bias were identified in the NLP pipeline. However the interconnection between sources
and overall impact of each source on downstream applications remains unclear. In this project, we
want to observe the entire pipeline, from the intrinsic point of view (within the model itself), to the
pre-training task point of view (in the case of autoregressive LLM, text generation), on to some real-
world downstream applications. We chose to focus on two types of medical applications: mental
illness diagnosis help and information extraction from clinical records for public health purposes
such as patient enrollment into clinical trials. The project will provide corpora and methods for a
global evaluation of bias in LLM in French as well as studies to further the understanding of biases
in clinical NLP pipelines and the environmental impact of the integration of these models in digital
health.

10.3.2 ANR Project: CoDeinE

Participants: Karën Fort, Bruno Guillaume, Bertrand Remy.

Title: artificial text COrpus DEsIgNed Ethically automatic synthesis of clinical documents

Duration: March 2021–February 2025

Coordinator: Aurélie Névéol (Limsi)

Partners: CRC, CEA List, LISN, LORIA

Participants: Bruno Guillaume, Karën Fort (local coordinator), Bertrand Remy

Abstract: Machine learning methods have become prevalent in language technologies. They rely on
annotated corpora to train models and evaluate algorithms. The CoDeinE project proposes to
address the lack of shareable corpora in sensitive domains such as health or banking. The key
idea of the project is to use confidential corpora to automatically generate synthetic texts that

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Inria Annual Report 2023

mimic the linguistic properties of real documents while preserving confidentiality. We will use
clinical documents in electronic patient records as a case study. Furthermore, the project will rely
on Games With A Purpose and crowd sourcing to validate and annotate the synthesized texts.

10.3.3 ANR Project: Autogramm

Participants:

Bruno Guillaume, Karën Fort, Guy Perrier, Khensa Amani Daoudi.

Title: Induction of descriptive grammar from annotated corpora

Duration: January 2022–December 2025

Coordinator: Sylvain Kahane (Université Paris Nanterre)

Partners: MoDyCo, LACITO, LISN, Inria Nancy – Grand Est

Participants: Bruno Guillaume (local coordinator), Karën Fort, Guy Perrier

Abstract: The goal of this project is to automate, as far as possible, the extraction of descriptive grammars
and grammatical descriptions from annotated corpora for linguistic and typological studies. The
project also promotes the development of treebanks for under-endowed languages, in order to
extract quantitative descriptive grammars for these languages. The project uses the annotation
scheme SUD (Surface-syntactic Universal Dependencies), the query tool Grew-match and the
annotation tool ArboratorGrew.

10.3.4 ANR Project: CODIM

Participants: Maxime Amblard, Jacques Jayez.

Title: Compositionality and discourse markers

Duration: January 2023–December 2026

Coordinator: Mathilde Dargnat (Université de Lorraine and ATILF)

Partners: ATIL, LLF, Inria Nancy – Grand Est

Participants: Maxime Amblard, Jacques Jayez

Abstract: The CODIM project focuses on the two main linguistic resources for organizing monologues
or conversations in human languages : D(iscourse) M(arkers)(therefore/donc, well/ben, bon etc. in
English/French) and prosody (in particular, intonation). It will evaluate their status with respect to
two major views on communication: compositionality (the possibility of combining meaningful
expressions into more complex meaningful expressions) and pattern or construction-based ap-
proaches (the idea that language users exploit partly ‘frozen’ strings of words). We will compare
the semantic and prosodic properties of simple and complex French DM (e.g. ah + bon) found
in corpora for written and spoken French, using a variety of technical tools for DM identification
(category-driven text mining), clustering (statistics and Machine Learning) and research in pros-
ody (duration and intensity measures, contour representation). The project fosters a number of
collaborations between linguists and computer scientists.

Project SEMAGRAMME

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11 Dissemination

Participants: Maxime Amblard, Marc Anderson, Hee-Soo Choi, Marie Cousin,
Amandine Decker, Philippe de Groote, Fanny Ducel, Karën Fort,
Bruno Guillaume, Jacques Jayez, Chuyuan Li, Michel Musiol, Guy Per-
rier, Sylvain Pogodalla, Valentin Richard, Vincent Tourneur.

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

• Maxime Amblard: main organiser of the 15th International Conference on Computational Se-

mantics, (IWCS 2023), Nancy 20-23 June 2023.

• Valentin Richard: local chair of the workshop Inquisitiveness Below and Beyond the Sentence

Boundary (InqBnB4), Nancy, 20 June 2023.

• The Sémagramme team organized a scientific memorial day in honor of their colleague Guy Perrier

who passed away in 2023.

General chair, scientific chair

• Karën Fort: co-organizer of the Journées scientifiques du GDR LIFT , Nancy, Nancy, 20-21 November

2023. [44]

Member of the organizing committees

• Bruno Guillaume, Hee-Soo Choi, Marie Cousin, Amandine Decker, Amandine Lecomte, Chuyuan
Li, Siyana Pavlova, Valentin Richard, Khensa Daoudi, Vincent Tourneur, Fanny Ducel, Julie Halbout,
and Laura Masson-Grehaigne: members of the organizing committee of the 15th International
Conference on Computational Semantics, (IWCS 2023), Nancy 20-23 June 2023.

11.1.2 Scientific events: selection

Chair of conference program committees

• Maxime Amblard: general chair of the 15th International Conference on Computational Semantics

(IWCS 2023), Nancy 20-23 June 2023.

Member of the conference program committees

• Philippe de Groote, Siyana Pavlova, and Valentin Richard: members of the program committee of

the 15th International Conference on Computational Semantics (IWCS 2023).

• Sylvain Pogodalla: member of the program committee of the 29th conference on traitement auto-

matique des langues naturelles (TALN 2023).

Reviewer

• Maxime Amblard: ARR 2023, ACL 2023, EMNLP 2023, JPC 2023, Lift, Semdial, SICon.

• Marc Anderson 12th International Workshop on Service Oriented, Holonic and Multi-Agent Manu-
facturing Systems for Industry of the Future (SOHOMA’22) Sustainability for the digital manufac-
turing era.

• Philippe de Groote: ACL Rolling Review (ARR).

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Inria Annual Report 2023

• Karën Fort: 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023),

traitement automatique des langues naturelles (TALN 2023).

• Sylvain Pogodalla: 28th Workshop on Logic, Language, Information and Computation (WoLLIC

2022).

• Valentin Richard: 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023).

11.1.3 Journal

Member of the editorial boards

• Maxime Amblard: Member of the editorial board and chief editor of the journal Traitement Auto-

matique des Langues, in charge of the pdf pipeline.

• Philippe de Groote: Area editor of the FoLLI-LNCS series.

• Michel Musiol: Psychological and educational sciences (Université d’ElOued Ed).

• Sylvain Pogodalla: Member of the editorial board of the journal Traitement Automatique des

Langues, in charge of the Résumés de thèses section.

Reviewer - reviewing activities

• Marc Anderson: Research Ethics: The International Journal of Research Ethics & Research Integrity,

Journal of Social Computing.

11.1.4 Invited talks

• Karën Fort: Ethics by design for real: lessons learned from an industry 4.0 European project. ERGO

IA, Biarritz, France, 12 October 2023 [13].

• Guy Perrier: Why is graph rewriting interesting for computational linguistics?. Keynote presentation

at GURT/SyntaxFest 2023, Washington D.C., 9 March 2023 [14].

11.1.5 Invited seminars

• Hee-Soo Choi: Corpus-based Language Universals Analysis using Universal Dependencies. Team
seminar Traitement Automatique du Langage Écrit et Parlé (TALEP), Laboratoire d’Informatique et
Systèmes (LIS), Marseille, France, 23 March 2023.

• Bruno Guillaume: Introduction to Grew-match. 1st UniDive webinar, online, 19 June 2023.

• Valentin Richard: Extraction des Interrogatives de Corpus Francophones Annotés en Dépendances

Universelles, Café TAL, Atilf, Nancy, 26 June 2023.

11.1.6 Leadership within the scientific community

• Maxime Amblard: Leader of OLKI2.0 project (Lorraine Université d’Excellence project - PIA).

• Karën Fort has been co-chair of the ACL Ethics Committee since 2021, with Min-Yen Kan (Univ. of

Singapore) and Y. Tsvetkov (Univ. of Washington).

• Bruno Guillaume is Working Group leader in a CA21167 - Universality, diversity and idiosyncrasy in

language technology (UniDive).

11.1.7 Scientific expertise

• Maxime Amblard: evaluation for the ANR generic call 2023.

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11.1.8 Research administration

• Maxime Amblard:

– Head of the master in Natural Language Processing (master 1 and 2).

– Member of CNU 27 (Computer Science).

• Karën Fort:

– Member of CNU 27 (Computer Science): participation to qualifications, promotions and suivi

de carrière.

• Bruno Guillaume:

– Head of the Natural Language Processing and Knowledge Discovery department of the LORIA

laboratory.

– Manager (with Alain Polguère) of the CPER (Contrat de Plan État-Région) Langues, Connais-

sances et Humanités Numériques.

• Sylvain Pogodalla:

– Elected member of the comité de centre Inria Nancy – Grand Est.

– In charge of the local commission IES (information et édition scientifique) of the Inria Nancy –

Grand Est and LORIA.

– Member of the national commission IES of Inria.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• Licence:

– Maxime Amblard, AI Introduction, 36h, L1, Université de Lorraine, France.

– Maxime Amblard, Chuyuan Li and Marie Cousin NLP for beginners, 20h, L2, Université de

Lorraine, France.

– Maxime Amblard and Chuyuan Li, Linguistic engineering, 20h, L3, Université de Lorraine,

France.

– Hee-Soo Choi, Databases, 14h, Telecom Nancy, Université de Lorraine, France.

– Amandine Decker, Research Methods in Linguistics, 10h, University of Gothenburg, Sweden.

– Karën Fort, Relational databases, 55h, L3, Sorbonne Université, France.

– Pierre Ludmann, Informatics 2, 27h, Mines Nancy, Université de Lorraine, France.

– Pierre Ludmann, Databases, 114h, Polytech Nancy, Université de Lorraine, France.

– Pierre Ludmann, Java project, 16h, Polytech Nancy, Université de Lorraine, France.

– Pierre Ludmann, Year-long project, 20h, Polytech Nancy, Université de Lorraine, France.

– Pierre Ludmann, Mentoring, 40h, Polytech Nancy, Université de Lorraine, France.

– Siyana Pavlova, Algorithmique - Programmation 1, 44h, L1, Université de Lorraine, France.

– Siyana Pavlova, Bases de données, 28h, L2, Université de Lorraine, France.

– Valentin Richard, Langages de Scripts, 50h, L3, Université de Lorraine, France.

– Vincent Tourneur, Structures de données, 40h, L1, IUT Charlemagne, Université de Lorraine,

France.

• Master:

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Inria Annual Report 2023

– Maxime Amblard, Siyana Pavlova and Fanny Ducel, Python Programming, 30h, M1 NLP

(IDMC), Université de Lorraine, France.

– Maxime Amblard and Siyana Pavlova, Methods for NLP, 36h, M1 NLP (IDMC), Université de

Lorraine, France.

– Maxime Amblard, NLP project, 20h, M1 NLP (IDMC), Université de Lorraine, France.

– Maxime Amblard, Marie Cousin, and Amandine Decker, Formalisms and Syntax, 24h, M2

NLP (IDMC), Université de Lorraine, France.

– Maxime Amblard, Valentin Richard and Siyana Pavlova, Discourse and Dialogue, 18h, M2 NLP

(IDMC), Université de Lorraine, France.

– Marie Cousin, Agile Method and Scrum, 4h, M2 NLP (IDMC), Université de Lorraine, France.

– Marie Cousin and Amandine Decker, Data Structures, 20h, M1 NLP (IDMC), Université de

Lorraine, France

– Philippe de Groote, Formal Logic, 22h, M1 NLP (IDMC), Université de Lorraine, France.

– Philippe de Groote, Formal languages, 22h, M1 NLP (IDMC), Université de Lorraine, France.

– Philippe de Groote, Computational Semantics, 18h, M2 NLP (IDMC), Université de Lorraine,

France.

– Philippe de Groote, Computational structures and logics for natural language modeling, 18h,

M2 NLP (IDMC), Université Paris Diderot – Paris 7, France.

– Karën Fort, Data Ethics, 18h, M1 ISF (Informatique et Statistique financières), Université

Panthéon Assas, France.

– Karën Fort, Ethics and NLP (English), 17h30, M2 NLP (IDMC), Université de Lorraine, France.

– Karën Fort, Formal Grammar, 39h, M1, Sorbonne Université, France.

– Karën Fort, Corpora, resources and tools for linguistics, 39h, M1, Sorbonne Université, France.

– Karën Fort, Ethics and NLP, 15h, M2, Sorbonne Université, France.

– Karën Fort, Collaborative annotation for NLP, 30h, M2, Sorbonne Université, France.

– Bruno Guillaume, Written Corpora TAL (English), 45h, M1 NLP (IDMC), Université de Lorraine,

France.

– Chuyuan Li, Introduction to G5K (English), 4h, M1 NLP (IDMC), Université de Lorraine,

France.

– Pierre Ludmann, Software Engineering 2, 10h, Mines Nancy, Université de Lorraine, France.

– Pierre Ludmann, Compilation, 27.5h, Mines Nancy, Université de Lorraine, France.

– Pierre Ludmann, Department project, 5h, Mines Nancy, Université de Lorraine, France.

– Pierre Ludmann, Software Engineering, 42h, Polytech Nancy, Université de Lorraine, France.

– Pierre Ludmann, Web project, 6h, Polytech Nancy, Université de Lorraine, France.

– Pierre Ludmann, End-of-study internships, 20h Polytech Nancy, Université de Lorraine,

France.

– Pierre Ludmann, End-of-study project, 20h Polytech Nancy, Université de Lorraine, France.

– Pierre Ludmann, Mentoring, 48h, Polytech Nancy, Université de Lorraine, France.

– Vincent Tourneur, UML beginners (English), 10h, M1 NLP (IDMC), Université de Lorraine,

France.

• Doctorate:

– Karën Fort, Scientific integrity, 6h, École doctorale 5, Faculté des lettres, Sorbonne Université.

– Karën Fort, Ethics and biases in NLP, Summer school of the GDR TAL, ETAL June 2023 –

Marseilles, France.

Project SEMAGRAMME

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• International Summer School:

– Bruno Guillaume with Kim Gerdes, Treebanking: methodology, tools and applications at
the 34th European Summer School in Logic, Language and Information (ESSLLI 2023) in
Ljubljana, Slovenia.

• International Tutorials:

– Karën Fort with Lucianna Benotti (Universidad Nacional de Córdoba, Argentine), Yulia Ts-
vetkov and Min-Yen Kan. Tutorial at EACL 2023 (CORE A): Understanding Ethics in NLP
Authoring and Reviewing (Introductory) [51].

11.2.2 Supervision

PhD

Chuyuan Li, Formal and Statistical Modeling of Dialogue, since October 2019. Supervision: Maxime

Amblard and Chloé Braud (IRIT).

PhD in progress

• Samuel Buchel, Linguistic, Semantic and Cognitive Modeling of Dialogical In-
congruities and Discontinuities in The Interaction with The Schizophrenic Patients, since
December 2019. Supervision: Maxime Amblard and Michel Musiol.

• Hee-Soo Choi, Lier des ressources lexicales du français en vue d’une interopérabilité entre
niveaux linguistiques, since October 2021. Supervision: Karën Fort and Mathieu Constant.

• Marie Cousin, Modélisation de paraphrase dans les grammaires catégorielles abstraites, since

October 2022. Supervision: Philippe de Groote and Sylvain Pogodalla.

• Amandine Decker, Modelling Topic-level Interaction in Pathological Conversations, since
October 2022. Supervision: Maxime Amblard and Ellen Breitholtz (University of Gothenburg,
Sweden).

• Fanny Ducel, Evaluating stereotyped biases in auto-regressive language models, since October

2023. Supervision: Karën Fort and Aurélie Névéol (LISN-CNRS).

• Maxime Guillaume, Structures de traits pour les Grammaires Catégorielles Abstraites, since

July 2021. Supervision: Philippe de Groote and Raphaël Salmon (Yseop).

• Nicolas Hiebel, Création éthique de données textuelles artificielles : application au domaine
biomédical, since October 2021. Supervision: Aurélie Névéol (LISN-CNRS), Karën Fort and
Olivier Ferret (CEA).

• Amandine Lecomte, Analyse longitudinale de prise en charge psychothérapeutique de patients
psychiatriques et de patients atteints de maladies neurodégénératives : informatisation et
modélisation dialogique des indices comportementaux associés à l’efficacité (vs échec) des
stratégies de prise en charge tentées par les thérapeutes, since October 2019. Supervision:
Michel Musiol and Alexandra König.

• Pierre Ludmann, Dynamic Construction of Discursive Structures, since September 2017. Su-

pervision: Philippe de Groote and Sylvain Pogodalla.

• Siyana Pavlova, Tools and Methods for Semantic Annotation, since November 2020. Supervi-

sion: Maxime Amblard and Bruno Guillaume.

• Valentin Richard, Aspects compositionnels et dynamiques de la sémantique inquisitrice, since
September 2021. Supervision: Philippe de Groote, Floris Roelofsen and Reinhart Muskens
(Universiteit van Amsterdam, ILLC).

• Priyansh Trivedi, Injecting Lexical and Semantic Knowledge into Word, Phrasal and Sentence
Embeddings, since November 2021. Supervision: Philippe de Groote and Pascal Denis (until
May 2023).

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11.2.3 Juries

Inria Annual Report 2023

• Maxime Amblard. PhD reviewer (rapporteur) for Nesrine Bannour’s PhD Information Extraction
from Electronic Health Records: Studies on temporal ordering, privacy and environmental impact,
Université Paris-Saclay, France, 3 November 2023.

• Maxime Amblard. PhD jury for Nicolas Devatine’s PhD Discourse-Driven Prediction and Character-

ization of Textual Bias, Université Toulouse III - Paul Sabatier, France, 23 October 2023.

• Karën Fort. PhD reviewer (rapporteuse) for Nicolas Devatine’s PhD Discourse-Driven Prediction
and Characterization of Textual Bias, Université Toulouse III - Paul Sabatier, France, 23 October
2023.

11.3 Popularization

11.3.1 Internal or external Inria responsibilities

• Maxime Amblard is a member of the scientific committee of )i( interstices.

11.3.2 Articles and contents

• Karën Fort. Comment les "BigTech" investissent la recherche en Traitement automatique des langues ?

INS2I Web site, CNRS, France, 12 September 2023.

• Karën Fort. Interstices podcast. Quelle éthique pour les agents conversationnels ? 28 August

2023 [53].

11.3.3 Education

• Marie Cousin and Amandine Decker: animation of a MATh.en.JEANS workshop within Edmond de

Goncourt secondary school in Pulnoy.

11.3.4 Interventions

• Maxime Amblard. L’IA n’ira pas beaucoup plus loin. Oxford Style debate, Forum des Sciences

Cognitves et du TAL, IDMC 29 November 2023.

• Maxime Amblard and Michel Musiol. Le repérage des signes de la pathologie mentale dans le discours,

Cycle de conférences sur le Handicap, Inria, 6 April 2023.

• Maxime Amblard. Je parle à mon ordinateur, c’est grave docteur ?, cycle de conférence A votre santé,

16 March 2023.

• Karën Fort. Les enjeux éthiques de l’IA vus par le prisme du traitement automatique des langues. In
Séminaire #3 IA: Enjeux éthiques et juridiques, ARCEP (Autorité de régulation des communications
électroniques, des postes et de la distribution de la presse). Paris. 15 December 2023.

• Karën Fort, Hee-Soo Choi and Fanny Ducel: 2023-12-07, presentation to high school students in

the context of the "Chiche !" initiative (lycée Varoquaux, Tomblaine, France).

• Karën Fort and Hee-Soo Choi: 2023-12-01, presentation to high school students in the context of

the "Chiche !" initiative (lycée Claude Gellée, Épinal, France).

• Marie Cousin and Amandine Decker: 2023-02-01 and 2023-02-02, presentation to high school

students in the context of the "Chiche !" initiative (lycée Cormontaigne, Metz, France).

• Marie Cousin, Amandine Decker and Vincent Tourneur: 2023-03-02 and 2023-03-03, presentation
to high school students in the context of the "Chiche !" initiative (lycée Jean-Victor Poncelet,
Saint-Avold, France).

Project SEMAGRAMME

27

• Amandine Decker: 2023-10-24, talks with high school students in the context of the first edition of
the “Les cigognes” mathematics and computer science research retreat in the Grand-Est region.

• Valentin Richard gave a talk for Pint of Science at Taverne de l’Irlandais (Nancy) on 22 May 2023.

• Valentin Richard was interviewed by the newspaper L’Est Républicain about his participation to

Pint of Science, 19 May 2023.

• Valentin Richard gave a talk [50] for the round-table discussion organized by CJD Section de Nancy

at Télécom Nancy (Nancy), on 16 October 2023.

12 Scientific production

12.1 Major publications

[1] M. Abdalla, J. P. Wahle, T. Ruas, A. Névéol, F. Ducel, S. M. Mohammad and K. Fort. ‘The Elephant
in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research’. In:
Proceedings of the 61st Annual Meeting of the Association for Computational LinguisticsVolume 1:
Long Papers. 61st Annual Meeting of the Association for Computational Linguistics. Vol. 1. Toronto,
Canada: Association for Computational Linguitics, 2023, pp. 13141–13160. URL: https://inria
.hal.science/hal-04105994.

[2] M. Anderson and K. Fort. ‘Human Where? A New Scale Defining Human Involvement in Technology
Communities from an Ethical Standpoint’. In: International Review of Information Ethics (25th Aug.
2022). URL: https://hal.inria.fr/hal-03762035.

[3] G. Bonfante and B. Guillaume. ‘Non-size increasing Graph Rewriting for Natural Language Pro-
cessing’. In: Mathematical Structures in Computer Science 28.08 (2018), pp. 1451–1484. DOI: 10.10
17/S0960129518000178. URL: https://hal.inria.fr/hal-00921038.

[4] G. Bonfante, B. Guillaume and G. Perrier. Application of Graph Rewriting to Natural Language
Processing. Vol. 1. Logic, Linguistics and Computer Science Set. ISTE Wiley, 2018, p. 272. URL:
https://hal.inria.fr/hal-01814386.

[5] P. de Groote and M. Kanazawa. ‘A Note on Intensionalization’. In: Journal of Logic, Language and
Information 22.2 (2013), pp. 173–194. DOI: 10.1007/s10849-013-9173-9. URL: https://hal.i
nria.fr/hal-00909207.

[6] A. Névéol, Y. Dupont, J. Bezançon and K. Fort. ‘French CrowS-Pairs: Extending a challenge dataset
for measuring social bias in masked language models to a language other than English’. In: ACL
2022 - 60th Annual Meeting of the Association for Computational Linguistics. Dublin, Ireland,
22nd May 2022. URL: https://hal.inria.fr/hal-03629677.

[7] S. Pogodalla. ‘A syntax-semantics interface for Tree-Adjoining Grammars through Abstract Cat-
egorial Grammars’. In: Journal of Language Modelling 5.3 (2017), pp. 527–605. DOI: 10.15398/jlm
.v5i3.193. URL: https://hal.inria.fr/hal-01242154.

[8] R. Reinecke, T. A. Nazir, S. Carvallo and J. Jayez. ‘Factives at hand: When presupposition mode
affects motor response’. In: Journal of Experimental Psychology (2022). DOI: 10.1037/xge0001167.
URL: https://hal.archives-ouvertes.fr/hal-03538732.

12.2 Publications of the year

International journals

[9] M. M. Anderson. ‘How we will Discover Sentience in AI’. In: Journal of Social Computing 4.3 (2023).

URL: https://hal.science/hal-04326728.

[10] M. M. Anderson. ‘Rare Opportunity or History Revisited? The Pitfalls and Prospects for Ethical AI in
light of Public Ethical Responses to the Telegraph’. In: Studia Philosophica Wratislaviensia 18.3
(2023). URL: https://hal.science/hal-04326649.

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[11] M. M. Anderson and K. Fort. ‘Evaluating the acceptability of ethical recommendations in industry
4.0: an ethics by design approach’. In: AI & Society: Knowledge, Culture and Communication
(12th Jan. 2024). DOI: 10.1007/s00146-023-01834-7. URL: https://inria.hal.science/ha
l-04390447.

[12] E. Ettore, P. Müller, J. Hinze, M. Riemenschneider, M. Benoit, B. Giordana, D. Postin, R. Hurlemann,
A. Lecomte, M. Musiol, H. Lindsay, P. Robert and A. König. ‘Digital Phenotyping for Differential
Diagnosis of Major Depressive Episode: Narrative Review’. In: JMIR Mental Health 10 (23rd Jan.
2023), e37225. DOI: 10.2196/37225. URL: https://hal.science/hal-04402917.

Invited conferences

[13] K. Fort. ‘Ethics by design for real : lessons learned from an industry 4.0 European project’. In:
ERGO’IA. Biarritz (France), France, 10th Oct. 2023. URL: https://inria.hal.science/hal-043
66784.

[14] G. Perrier. ‘Why is graph rewriting interesting for computational linguistics?’ In: GURT/SyntaxFest
2023. Washington, United States, 9th Mar. 2023. URL: https://inria.hal.science/hal-04400
656.

International peer-reviewed conferences

[15] M. Abdalla, J. P. Wahle, T. Ruas, A. Névéol, F. Ducel, S. M. Mohammad and K. Fort. ‘The Elephant
in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research’. In:
Proceedings of the 61st Annual Meeting of the Association for Computational LinguisticsVolume 1:
Long Papers. 61st Annual Meeting of the Association for Computational Linguistics. Vol. 1. Toronto,
Canada: Association for Computational Linguitics, 2023, pp. 13141–13160. URL: https://inria
.hal.science/hal-04105994.

[16] M. Cousin. ‘Meaning-Text Theory within Abstract Categorial Grammars: Towards Paraphrase
and Lexical Function Modeling for Text Generation’. In: Proceedings of the 15th International
Conference on Computational Semantics (IWCS). IWCS 2023 - 15th International Conference on
Computational Semantics. Nancy, France: Association for Computational Linguistics, 20th June
2023. URL: https://inria.hal.science/hal-04104453.

[17] A. Decker and M. Amblard. ‘Analysing topic shifts in task-oriented dialogues’. In: Journées scienti-
fiques du GDR Lift - LIFT 2023. Nancy, France, 20th Nov. 2023. URL: https://inria.hal.scienc
e/hal-04356426.

[18] A. Decker, E. Breitholtz, C. Howes and S. Larsson. ‘Topic and genre in dialogue’. In: Proceedings of
the 27th Workshop on the Semantics and Pragmatics of Dialogue. SEMDIAL 2023. Maribor, Slovenia,
16th Aug. 2023, pp. 143–145. URL: https://hal.science/hal-04323083.

[19]

I. Fernandez, K. Lopez de Calle, E. Garate, R. Benzmuller, M. Kessler and M. Anderson. ‘Human-
Feedback for AI in Industry’. In: CENTRIC 2023The Sixteenth International Conference on Advances
in Human oriented andPersonalized Mechanisms, Technologies, and Services. CENTRIC 2023, The
Sixteenth International Conference on Advances in Human-oriented and Personalized Mech-
anisms, Technologies, and Services. CENTRIC 2023 The Sixteenth International Conference on
Advances in Human oriented and Personalized Mechanisms, Technologies, and Services. Valencia,
Spain, 17th Nov. 2023. URL: https://hal.science/hal-04326432.

[20] B. Guillaume. ‘Graph-based multi-layer querying in Parseme Corpora’. In: Proceedings of the 19th
Workshop on Multiword Expressions (MWE 2023). Dubrovnic, Croatia: Association for Computa-
tional Linguistics, 5th June 2023, pp. 58–64. DOI: 10.18653/v1/2023.mwe-1.9. URL: https://in
ria.hal.science/hal-04387852.

[21] N. Hiebel, O. Ferret, K. Fort and A. Névéol. ‘Can Synthetic Text Help Clinical Named Entity Recogni-
tion? A Study of Electronic Health Records in French’. In: The 17th Conference of the European
Chapter of the Association for Computational Linguistics (EACL 2023). Dubrovnik, Croatia, 2nd May
2023. DOI: 10.18653/v1/2023.eacl-main.170. URL: https://inria.hal.science/hal-040
18935.

Project SEMAGRAMME

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[22] C. Li, M. Amblard and C. Braud. ‘A Semi-supervised Dialogue Discourse Parsing Pipeline’. In:
Journées Scientifiques du GDR Lift (LIFT 2023). Nancy, France, 20th Nov. 2023. URL: https://inr
ia.hal.science/hal-04356416.

[23] C. Li, P. Huber, W. Xiao, M. Amblard, C. Braud and G. Carenini. ‘Discourse Structure Extraction from
Pre-Trained and Fine-Tuned Language Models in Dialogues’. In: Proceedings of the 17th Conference
of the European Chapter of the Association for Computational Linguistics (EACL 2023). European
Chapter of the Association for Computational Linguistics (EACL). Dubrovnik, Croatia, 2nd May
2023, pp. 2562–2579. URL: https://hal.univ-lorraine.fr/hal-04031267.

[24] S. Pavlova, M. Amblard and B. Guillaume. ‘Bridging Semantic Frameworks: mapping DRS onto
AMR’. In: Proceedings of The 15th International Conference on Computational Semantics (IWCS
2023). The 15th International Conference on Computational Semantics (IWCS 2023). Nancy, France,
20th June 2023. URL: https://inria.hal.science/hal-04129563.

[25]

S. Pavlova, M. Amblard and B. Guillaume. ‘Structural and Global Features for Comparing Semantic
Representation Formalisms’. In: Proceedings of the 4th International Workshop on Designing Mean-
ing Representation (DMR 2023). The 4th International Workshop on Designing Meaning Represent-
ation. Nancy, France, 20th June 2023. URL: https://inria.hal.science/hal-04129557.

National peer-reviewed Conferences

[26] V.-T. Barrouillet, M. Amblard and M. Musiol. ‘Toward an automatic identification of discontinuities
in the pathological discourse of patient with schizophrenia’. In: Journées scientifiques du GDR Lift
- LIFT 2023. Nancy, France, 20th Nov. 2023. URL: https://inria.hal.science/hal-04356448.

[27] H.-S. Choi, K. Fort, B. Guillaume and M. Constant. ‘Des ressources lexicales du français et de
leur utilisation en TAL : étude des actes de TALN’. In: Actes de CORIA-TALN 2023. Actes de la 30e
Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 2 : travaux de
recherche originaux – articles courts. 18e Conférence en Recherche d’Information et Applications
– 16e Rencontres Jeunes Chercheurs en RI – 30e Conférence sur le Traitement Automatique des
Langues Naturelles – 25e Rencontre des Étudiants Chercheurs en Informatique pour le Traitement
Automatique des Langues. Paris, France: ATALA, 2023, pp. 23–36. URL: https://hal.science/ha
l-04130208.

[28] M. Cousin. ‘Towards an implementation of meaning-text theory with abstract categorial grammars’.
In: Actes de CORIA-TALN 2023. Actes des 16e Rencontres Jeunes Chercheurs en RI (RJCRI) et 25e
Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues
(RÉCITAL). 18e Conférence en Recherche d’Information et Applications – 16e Rencontres Jeunes
Chercheurs en RI – 30e Conférence sur le Traitement Automatique des Langues Naturelles – 25e
Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues.
Paris, France: ATALA, 2023, pp. 72–86. URL: https://inria.hal.science/hal-04100197.

[29] N. Hiebel, O. Ferret, K. Fort and A. Névéol. ‘Les textes cliniques français générés sont-ils dangereuse-
ment similaires à leur source ? Analyse par plongements de phrases’. In: Actes de CORIA-TALN 2023.
Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume
2 : travaux de recherche originaux – articles courts. 18e Conférence en Recherche d’Information
et Applications – 16e Rencontres Jeunes Chercheurs en RI – 30e Conférence sur le Traitement
Automatique des Langues Naturelles – 25e Rencontre des Étudiants Chercheurs en Informatique
pour le Traitement Automatique des Langues. Paris, France: ATALA, 2023, pp. 46–54. URL: https:
//hal.science/hal-04130203.

[30] N. Hiebel, O. Ferret, K. Fort and A. Névéol. ‘Similarité surfacique et similarité sémantique dans des
cas cliniques générés’. In: Journée d’étude sur la Similarité entre Patients, ATALA, SimPa 2023. Paris,
France, 13th Mar. 2023. URL: https://hal.science/hal-04102816.

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Inria Annual Report 2023

[31] S. Kahane, S. Herrera, B. Guillaume and K. Gerdes. ‘Autogramm : développement simultané de
treebanks et de grammaires à partir de corpus’. In: Actes de CORIA-TALN 2023. Actes de la 30e
Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 6 : projets. 18e
Conférence en Recherche d’Information et Applications, 16e Rencontres Jeunes Chercheurs en
RI, 30e Conférence sur le Traitement Automatique des Langues Naturelles, 25e Rencontre des
Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues. Paris, France:
ATALA, 2023, pp. 37–42. URL: https://hal.science/hal-04208598.

[32] S. Pavlova, M. Amblard and B. Guillaume. ‘A Layered Approach to Semantic Representation’. In:
Journées scientifiques du GDR Lift - LIFT 2023. Nancy, France, 20th Nov. 2023. URL: https://inri
a.hal.science/hal-04356436.

[33] V. D. Richard. ‘Can French Interrogative Retrieval be Fully Machine-Based ?’ In: Actes des journées
LIFT 2023. 5èmes journées du Groupement de Recherche CNRS « Linguistique Informatique,
Formelle et de Terrain » (LIFT 2023). Nancy, France, 20th Nov. 2023, pp. 69–76. URL: https://hal
.science/hal-04359947.

Conferences without proceedings

[34] M. M. Anderson. ‘AI as Philosophical Ideology: A Critical look back at McCarthy’s Program’. In:
Philosophy in Technology Workshop 2nd Edition. Wrocław, Poland, 28th Apr. 2023. URL: https:
//inria.hal.science/hal-04364525.

[35] M. M. Anderson. ‘AI Ethics and the Lessons of History’. In: 2nd International Conference on the
Ethics of Artificial Intelligence. Porto, Portugal, 28th Nov. 2023. URL: https://inria.hal.scienc
e/hal-04364510.

[36] F. Ducel, A. Névéol and K. Fort. ‘Bias Identification in Language Models is Biased’. In: Workshop on
Algorithmic Injustice 2023. Amsterdam, Netherlands, 26th June 2023. URL: https://inria.hal
.science/hal-04171198.

[37] P. de Groote. ‘On the semantics of dependencies: relative clauses and open clausal complements
- extended abstract -’. In: Logic and Engineering of Natural Language Semantics 20 (LENLS20).
Osaka, Japan, 18th Nov. 2023. URL: https://inria.hal.science/hal-04402969.

[38] P. de Groote, M. Guillaume, A. Helman, S. Pogodalla and R. Salmon. ‘Extending Abstract Categorial
Grammars with Feature Structures: Theory and Practice’. In: Logic and Engineering of Natural
Language Semantics 20 (LENLS20). Osaka, Japan, 18th Nov. 2023. URL: https://inria.hal.sci
ence/hal-04328753.

[39]

J. Jayez. ‘(Innocent?) Bias in Argumentation’. In: IMPAQTS (Implicit Manipulation in Politics –
Quantitatively Assessing the Tendentiousness of Speeches) final conference. Rome, Italy, 27th Apr.
2023. URL: https://hal.science/hal-04393407.

[40] A. Savary, C. Ben Khelil, C. Ramisch, V. Giouli, V. Barbu Mititelu, N. Hadj Mohamed, C. Krstev, C.
Liebeskind, H. Xu, S. Stymne, T. Güngör, T. Pickard, B. Guillaume, A. Bhatia, M. Candito, P. Gantar,
U. Iñurrieta, A. Gatt, K. Jolanta, K. Simon, T. Lichte, N. Ljubeši´c, J. Monti, C. Parra Escartín, M.
Shamsfard, I. Stoyanova, V. Vincze and A. Walsh. ‘PARSEME corpus release 1.3’. In: Proceedings
of the 19th Workshop on Multiword Expressions (MWE 2023). Dubrovnik, Slovenia: Association
for Computational Linguistics, 6th May 2023, pp. 24–35. DOI: 10.18653/v1/2023.mwe-1.6. URL:
https://hal.science/hal-04323219.

Scientific book chapters

[41] K. Gerdes, B. Guillaume, S. Kahane and G. Perrier. ‘Function words in Surface-Syntactic Universal
Dependencies’. In: Function words in dependency syntax. John Benjamins Publishing Company,
2023. URL: https://hal.parisnanterre.fr/hal-04067950.

Project SEMAGRAMME

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[42] P. de Groote. ‘Deriving Formal Semantic Representations from Dependency Structures’. In: Logic
and Engineering of Natural Language Semantics,19th International Conference, LENLS19, Tokyo,
Japan, November 19–21, 2022, Revised Selected Papers. Vol. 14213. Lecture Notes in Computer
Science. Springer, 24th Oct. 2023, pp. 157–172. DOI: 10.1007/978- 3- 031- 43977- 3_10. URL:
https://inria.hal.science/hal-04403076.

Edition (books, proceedings, special issue of a journal)

[43] M. M. Anderson, ed. Exploring the Idea of Ethical Sustainability for Digital Manufacturing. Service
Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA
2023. Studies in Computational Intelligence, Springer. 2023. URL: https://hal.science/hal-0
4326602.

[44] K. Fort, C. Gardent and Y. Parmentier, eds. Actes des 5èmes journées du Groupement de Recherche
CNRS « Linguistique Informatique, Formelle et de Terrain ». Nov. 2023, p. 135. URL: https://hal.s
cience/hal-04313917.

[45] Lambek–Grishin Calculus: Focusing, Display and Full Polarization. Samson Abramsky on Logic
and Structure in Computer Science and Beyond 25 (2nd Aug. 2023). DOI: 10.1007/978-3-031-24
117-8_24. URL: https://hal.science/hal-04358451.

[46] V. D. Richard and F. Roelofsen, eds. Proceedings of the 4th Workshop on Inquisitiveness Below and
Beyond the Sentence Boundary. 4th Workshop on Inquisitiveness Below and Beyond the Sentence
Boundary (InqBnB4). Association for Computational Linguistics, 2023. URL: https://hal.scien
ce/hal-04358496.

Doctoral dissertations and habilitation theses

[47] C. Li. ‘Facing Data Scarcity in Dialogues for Discourse Structure Discovery and Prediction’. Uni-
versité de Lorraine, 24th Aug. 2023. URL: https://theses.hal.science/tel-04218515.

Reports & preprints

[48]

J. Jayez. Argumentation et probabilités, ou pourquoi l’argumentation rationnelle n’est pas (toujours)
un raisonnement. 6th Dec. 2023. URL: https://hal.science/hal-04393794.

[49] Y. Parmentier, S. Pogodalla, R. Bawden, M. Labeau and I. Eshkol-Taravella. Procédure de diffusion
des publications de l’ATALA sur les archives ouvertes. ATALA, Sept. 2023, p. 17. URL: https://hal.s
cience/hal-04258177.

[50] V. D. Richard. The costs of Artificial Intelligence: Un aperçu des problèmes éthiques de l’IA. 17th Oct.

2023. URL: https://hal.science/hal-04245627.

Other scientific publications

[51] L. Benotti, K. Fort, M.-Y. Kan and Y. Tsvetkov. Understanding Ethics in NLP Authoring and Reviewing.
Dubrovnic, Croatia, 6th May 2023. DOI: 10.18653/v1/2023.eacl-tutorials.4. URL: https:
//inria.hal.science/hal-04400002.

[52] B. Guillaume. ‘Multi-layer querying in Corpora: Example of Parseme and UD’. In: UniDive 1st
general meeting. Saclay, France, 16th Mar. 2023. URL: https://inria.hal.science/hal-04387
830.

12.3 Other

Scientific popularization

[53] K. Fort and J. Jongwane. Quelle éthique pour les agents conversationnels ? [podcast]. 4th Sept. 2023.

URL: https://inria.hal.science/hal-04230666.

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12.4 Cited publications

[54] M. Amblard, C. Braud and M. Musiol. ‘Mon ordinateur est-il un bon psy ? Le TAL au service du
diagnostic médical’. In: Journée du GDR TAL : Intelligence artificielle et technologies des langues :
l’ordinateur passe la barrière de la langue (2021). GDR TAL. Paris, France, Jan. 2021. URL: https:
//shs.hal.science/halshs-03187834.

[55] P. de Groote. ‘Towards a Montagovian account of dynamics’. In: Proceedings of the 16th Semantics
and Linguistic Theory Conference (SALT 16). Ed. by M. Gibson and J. Howell. 2006. DOI: 10.3765/s
alt.v16i0.2952.

[56] P. de Groote. ‘Towards abstract categorial grammars’. In: Association for Computational Linguistics,
39th Annual Meeting and 10th Conference of the European Chapter. Colloque avec actes et comité
de lecture. internationale. Toulouse, France, July 2001, pp. 148–155. URL: https://hal.inria.fr
/inria-00100529.

[57] P. de Groote and S. Pogodalla. ‘On the expressive power of Abstract Categorial Grammars: Rep-
resenting context-free formalisms’. In: Journal of Logic, Language and Information 13.4 (2004).
http://www.springerlink.com/content/1572-9583/, pp. 421–438. DOI: 10.1007/s10849-004-211
4-x. URL: https://hal.inria.fr/inria-00112956.

[58] B. Guillaume and G. Perrier. ‘Interaction Grammars’. In: Research on Language and Computation
7.2-4 (2009), pp. 171–208. DOI: 10.1007/s11168-010-9066-x. URL: https://hal.inria.fr/i
nria-00568888.

[59] A. Kuyumcuyan and M. Musiol. ‘L’entretien clinique avec la personne polyhandicapée : un terrain
commun sciences du langage / psychiatrie’. In: Les sciences du langage face aux défis de la discip-
linarisation et de l’interdisciplinarité. Malika Temmar, Marina Krylyschin, Guy Achard-Bayle (éds).
Jan. 2021. URL: https://inria.hal.science/hal-03439236.

[60] A. Lecomte, S. Buchel, N. Franck, C. Demily, M. Amblard and M. Musiol. ‘Organisations et fonctions
du comportement verbal de type “backchannels” dans l’interaction clinique avec la personne
souffrant de schizophrénie’. In: 8eme Congrès mondial de linguistique française. Orléans, France,
July 2022. URL: https://inria.hal.science/hal-03724063.

[61] P. Ludmann, S. Pogodalla and P. de Groote. ‘Multityped Abstract Categorial Grammars and Their
Composition’. In: WoLLIC 2022 - 28th International Workshop on Logic, Language, Information,
and Computation. Vol. 13468. Lecture Notes in Computer Science. Ia¸si, Romania: Springer Inter-
national Publishing, 9th Sept. 2022, pp. 105–122. DOI: 10.1007/978-3-031-15298-6_7. URL:
https://hal.inria.fr/hal-03781596.

[62]

I. Mel’ˇcuk. Semantics: From Meaning to Text. Vol. 1. Studies in Language Companion Series 129.
Amsterdam/Philadelphia: John Benjamins Publishing Company, 2012.

[63] M. Musiol. ‘Incohérence et formes psychopathologiques dans l’interaction verbale’. In: Psychose,

langage et action: Approches neuro-cognitives (2009), p. 217.

[64] M. Musiol and M. Rebuschi. ‘La rationalité de l’incohérence en conversation schizophrène’. In:
Psychologie Française in press (2006), in press. DOI: 10 . 1016 / j . psfr . 2006 . 03 . 001. URL:
https://hal.science/hal-00133354.

[65] M. Musiol, M. Rebuschi, S. Buchel, A. Lecomte, P. de Groote and M. Amblard. ‘Le problème de
l’analyse des troubles de la pensée dans le discours avec la personne schizophrène : proposition
méthodologique’. In: L’Évolution Psychiatrique 87.2 (Apr. 2022), pp. 347–369. DOI: 10.1016/j.evo
psy.2022.04.004. URL: https://inria.hal.science/hal-03724030.

[66]

S. Padroni, C. Demily, N. Franck, C. Bocerean, C. Hoffmann and M. Musiol. ‘Ajustement comporte-
mental et mouvements de saccades oculaires dans la schizophrénie’. In: L’Évolution Psychiatrique
81 (2016), pp. 365–379. DOI: 10.1016/j.evopsy.2016.01.008. URL: https://hal.science/ha
l-01497272.

[67] G. Perrier. ‘A French Interaction Grammar’. In: RANLP 2007 - International Conference on Recent
Advances in Natural Language Processing. Ed. by G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov
and K. Simov. IPP & BAS & ACL-Bulgaria. Borovets, Bulgaria: INCOMA Ltd, Shoumen, Bulgaria,
Sept. 2007, pp. 463–467. URL: https://hal.inria.fr/inria-00184108.

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[68] A. Trognon, M. Musiol, E. Tinti, B. Beaupain and J. Donadieu. ‘Updated norms of the MOS-SF36 in
the young French population’. In: BMC Psychology (2022). DOI: 10.21203/rs.3.rs-1006967/v1.
URL: https://hal.univ-lorraine.fr/hal-03583305.
<scratchpad>
- Main topic: The Sémagramme project, a research initiative focused on natural language processing and its applications.
- Key points:
  - The project aims to design and develop new unifying logic-based models, methods, and tools for the semantic analysis of natural language utterances and discourses.
  - The research focuses on three main areas: syntax-semantics interface, discourse dynamics, and common basic resources.
  - The project is organized along three research directions, which interact to provide a comprehensive approach.
  - The team has made significant progress in developing new software tools, such as ACGtk and Grew, and has contributed to various open data initiatives.
  - The project has also explored the use of logic-based approaches in discourse modeling and has addressed issues related to ethics and biases in NLP.
- Structure: The document is divided into several sections, including an activity report, project-team information, and research highlights.
- Potential artifacts to ignore: Web-related elements, such as links, HTML tags, and other web-related elements.
</scratchpad>

<final_summary>
The Sémagramme project is a research initiative focused on natural language processing and its applications. The project aims to design and develop new unifying logic-based models, methods, and tools for the semantic analysis of natural language utterances and discourses. The research focuses on three main areas: syntax-semantics interface, discourse dynamics, and common basic resources. The project has made significant progress in developing new software tools and has contributed to various open data initiatives. The team has also explored the use of logic-based approaches in discourse modeling and has addressed issues related to ethics and biases in NLP. Overall, the Sémagramme project provides a comprehensive approach to natural language processing, combining logic-based models with open data initiatives and exploring the use of logic-based approaches in discourse modeling.
The Sémagramme project is a research initiative focused on natural language processing and its applications. The project aims to design and develop new unifying logic-based models, methods, and tools for the semantic analysis of natural language utterances and discourses. The research focuses on three main areas: syntax-semantics interface, discourse dynamics, and common basic resources. The project has made significant progress in developing new software tools and has contributed to various open data initiatives. The team has also explored the use of logic-based approaches in discourse modeling and has addressed issues related to ethics and biases in NLP. Overall, the Sémagramme project provides a comprehensive approach to natural language processing, combining logic-based models with open data initiatives and exploring the use of logic-based approaches in discourse modeling.