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0 | How does their model learn using mostly raw data? | by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective pola... | Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (N... |
2 | How did the select the 300 Reddit communities for comparison? | They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language. | A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities... | “If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.” — Italo Calvino, Invisible Cities A community's identity—defined through the common interests and shared experiences of its users—shapes... |
3 | Is all text in this dataset a question, or are there unrelated sentences in between questions? | the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences | Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task t... | Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the spe... |
4 | What aspects have been compared between various language models? | Quality measures using perplexity and recall, and performance measured using latency and energy usage. | In recent years, we have witnessed a dramatic shift towards techniques driven by neural networks for a variety of NLP tasks. Undoubtedly, neural language models (NLMs) have reduced perplexity by impressive amounts. This progress, however, comes at a substantial cost in performance, in terms of inference latency and ene... | Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs)... |
6 | How many attention layers are there in their model? | one | Saliency map generation techniques are at the forefront of explainable AI literature for a broad range of machine learning applications. Our goal is to question the limits of these approaches on more complex tasks. In this paper we apply Layer-Wise Relevance Propagation (LRP) to a sequence-to-sequence attention model t... | Ever since the LIME algorithm BIBREF0 , "explanation" techniques focusing on finding the importance of input features in regard of a specific prediction have soared and we now have many ways of finding saliency maps (also called heat-maps because of the way we like to visualize them). We are interested in this paper by... |
7 | What are the three measures of bias which are reduced in experiments? | RIPA, Neighborhood Metric, WEAT | It has been shown that word embeddings derived from large corpora tend to incorporate biases present in their training data. Various methods for mitigating these biases have been proposed, but recent work has demonstrated that these methods hide but fail to truly remove the biases, which can still be observed in word n... | Word embeddings, or vector representations of words, are an important component of Natural Language Processing (NLP) models and necessary for many downstream tasks. However, word embeddings, including embeddings commonly deployed for public use, have been shown to exhibit unwanted societal stereotypes and biases, raisi... |
10 | How big is the dataset? | 903019 references | In this paper, we introduce the citation data of the Czech apex courts (Supreme Court, Supreme Administrative Court and Constitutional Court). This dataset was automatically extracted from the corpus of texts of Czech court decisions - CzCDC 1.0. We obtained the citation data by building the natural language processing... | Analysis of the way court decisions refer to each other provides us with important insights into the decision-making process at courts. This is true both for the common law courts and for their counterparts in the countries belonging to the continental legal system. Citation data can be used for both qualitative and qu... |
11 | How is the intensity of the PTSD established? | Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity is established as mean squared error. | Veteran mental health is a significant national problem as large number of veterans are returning from the recent war in Iraq and continued military presence in Afghanistan. While significant existing works have investigated twitter posts-based Post Traumatic Stress Disorder (PTSD) assessment using blackbox machine lea... | Combat veterans diagnosed with PTSD are substantially more likely to engage in a number of high risk activities including engaging in interpersonal violence, attempting suicide, committing suicide, binge drinking, and drug abuse BIBREF0. Despite improved diagnostic screening, outpatient mental health and inpatient trea... |
13 | how is quality measured? | Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality. | In this paper, we introduce UniSent a universal sentiment lexica for 1000 languages created using an English sentiment lexicon and a massively parallel corpus in the Bible domain. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of number of covered languages, including many low ... | Sentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 languages BIBREF0 , preventing accurate sentiment classification in a low resource setup. Recent research efforts on cross-lingual transfer learning enable... |
15 | What is the accuracy reported by state-of-the-art methods? | Answer with content missing: (Table 1)
Previous state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages) | Language Identification (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a langua... | Language Identification (LI) is a problem which involves classifying the language being spoken by a speaker. LI systems can be used in call centers to route international calls to an operator who is fluent in that identified language BIBREF0. In speech-based assistants, LI acts as the first step which chooses the corre... |
19 | How do the authors define or exemplify 'incorrect words'? | typos in spellings or ungrammatical words | In this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding scheme in BERT, a powerful language representation model solely based o... | Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario i... |
21 | Which experiments are perfomed? | They used BERT-based models to detect subjective language in the WNC corpus | Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization. This bias is introduced in natural language via inflammatory words and phrases, casting doubt over facts, and presupposing the truth. In this work, we perform comprehens... | In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias is pervasiv... |
22 | Is ROUGE their only baseline? | No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU. | Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of natural language generation output at the sentence level. We further introduce WPSLOR, a novel WordPie... | Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings and, overall, leads to higher user satisfaction and u... |
24 | By how much does their system outperform the lexicon-based models? | Under the retrieval evaluation setting, their proposed model + IR2 had better MRR than NVDM by 0.3769, better MR by 4.6, and better Recall@10 by 20 .
Under the generative evaluation setting the proposed model + IR2 had better BLEU by 0.044 , better CIDEr by 0.033, better ROUGE by 0.032, and better METEOR by 0.029 | Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic comm... | Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and generating the natural language. Theref... |
29 | How are the main international development topics that states raise identified? | They focus on exclusivity and semantic coherence measures: Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. They select select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclusivit... | There is surprisingly little known about agenda setting for international development in the United Nations (UN) despite it having a significant influence on the process and outcomes of development efforts. This paper addresses this shortcoming using a novel approach that applies natural language processing techniques ... | Decisions made in international organisations are fundamental to international development efforts and initiatives. It is in these global governance arenas that the rules of the global economic system, which have a huge impact on development outcomes are agreed on; decisions are made about large-scale funding for devel... |
33 | Is the model evaluated? | the English version is evaluated. The German version evaluation is in progress | We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, w... | We developed a syntactic text simplification (TS) approach that can be used as a preprocessing step to facilitate and improve the performance of a wide range of artificial intelligence (AI) tasks, such as Machine Translation, Information Extraction (IE) or Text Summarization. Since shorter sentences are generally bette... |
35 | How better is accuracy of new model compared to previously reported models? | Average accuracy of proposed model vs best prevous result:
Single-task Training: 57.57 vs 55.06
Multi-task Training: 50.17 vs 50.59 | This paper addresses the problem of comprehending procedural commonsense knowledge. This is a challenging task as it requires identifying key entities, keeping track of their state changes, and understanding temporal and causal relations. Contrary to most of the previous work, in this study, we do not rely on strong in... | A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as it demands modeling the intrinsi... |
36 | How does the active learning model work? | Active learning methods has a learning engine (mainly used for training of classification problems) and the selection engine (which chooses samples that need to be relabeled by annotators from unlabeled data). Then, relabeled samples are added to training set for classifier to re-train, thus continuously improving the ... | Electronic health records (EHRs) stored in hospital information systems completely reflect the patients' diagnosis and treatment processes, which are essential to clinical data mining. Chinese word segmentation (CWS) is a fundamental and important task for Chinese natural language processing. Currently, most state-of-t... | Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patients, can be discovered BIBREF1 . Si... |
37 | Did the annotators agreed and how much? | For event types and participant types, there was a moderate to substantial level of agreement using the Fleiss' Kappa. For coreference chain annotation, there was average agreement of 90.5%. | This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. T... | A script is “a standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor” BIBREF0 . Script events describe an action/activity along with the involved participants. For example, in the script describing a visit to a restaurant, typical events are ... |
38 | What datasets are used to evaluate this approach? | Kinship and Nations knowledge graphs, YAGO3-10 and WN18KGs knowledge graphs | Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for li... | Knowledge graphs (KG) play a critical role in many real-world applications such as search, structured data management, recommendations, and question answering. Since KGs often suffer from incompleteness and noise in their facts (links), a number of recent techniques have proposed models that embed each entity and relat... |
40 | How was the dataset collected? | They crawled travel information from the Web to build a database, created a multi-domain goal generator from the database, collected dialogue between workers an automatically annotated dialogue acts. | To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restauran... | Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. However, the research is still largely limited by the availability of large-scale high-quality dialogue data. Many corpora have advanced the researc... |
41 | What models other than standalone BERT is new model compared to? | Only Bert base and Bert large are compared to proposed approach. | Pretraining deep contextualized representations using an unsupervised language modeling objective has led to large performance gains for a variety of NLP tasks. Despite this success, recent work by Schick and Schutze (2019) suggests that these architectures struggle to understand rare words. For context-independent wor... | As traditional word embedding algorithms BIBREF1 are known to struggle with rare words, several techniques for improving their representations have been proposed over the last few years. These approaches exploit either the contexts in which rare words occur BIBREF2, BIBREF3, BIBREF4, BIBREF5, their surface-form BIBREF6... |
42 | How big is the performance difference between this method and the baseline? | Comparing with the highest performing baseline: 1.3 points on ACE2004 dataset, 0.6 points on CWEB dataset, and 0.86 points in the average of all scores. | Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise ... | Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usually ambiguous, i.e., different named entities may share t... |
43 | What approaches without reinforcement learning have been tried? | classification, regression, neural methods | Task B Phase B of the 2019 BioASQ challenge focuses on biomedical question answering. Macquarie University's participation applies query-based multi-document extractive summarisation techniques to generate a multi-sentence answer given the question and the set of relevant snippets. In past participation we explored the... | The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to give an exact answer, usually a fact... |
44 | Which languages do they validate on? | Ar, Bg, Ca, Cs, Da, De, En, Es, Eu, Fa, Fi, Fr, Ga, He, Hi, Hu, It, La, Lt, Lv, Nb, Nl, Nn, PL, Pt, Ro, Ru, Sl, Sv, Tr, Uk, Ur | The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different a... | The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema, prescribing how features like gender or case should be ma... |
45 | What is the baseline method for the task? | For the emotion recognition from text they use described neural network as baseline.
For audio and face there is no baseline. | The recognition of emotions by humans is a complex process which considers multiple interacting signals such as facial expressions and both prosody and semantic content of utterances. Commonly, research on automatic recognition of emotions is, with few exceptions, limited to one modality. We describe an in-car experime... | Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utterance, a sentence, or a Tweet). The set of emotions is often following t... |
46 | what amounts of size were used on german-english? | Training data with 159000, 80000, 40000, 20000, 10000 and 5000 sentences, and 7584 sentences for development | It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these result... | While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or uns... |
48 | How big is the dataset? | Resulting dataset was 7934 messages for train and 700 messages for test. | With a sharp rise in fluency and users of "Hinglish" in linguistically diverse country, India, it has increasingly become important to analyze social content written in this language in platforms such as Twitter, Reddit, Facebook. This project focuses on using deep learning techniques to tackle a classification problem... | Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blend... |
53 | What MC abbreviate for? | machine comprehension | The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions... | Enabling computers to understand given documents and answer questions about their content has recently attracted intensive interest, including but not limited to the efforts as in BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Many specific problems such as machine comprehension and question answering ofte... |
54 | What are their correlation results? | High correlation results range from 0.472 to 0.936 | We propose SumQE, a novel Quality Estimation model for summarization based on BERT. The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references. SumQE achieves very high correlations with human rat... | Quality Estimation (QE) is a term used in machine translation (MT) to refer to methods that measure the quality of automatically translated text without relying on human references BIBREF0, BIBREF1. In this study, we address QE for summarization. Our proposed model, Sum-QE, successfully predicts linguistic qualities of... |
56 | What dataset do they use? | A parallel corpus where the source is an English expression of code and the target is Python code. | Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day’s object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itse... | Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, “Let us change our... |
57 | What is typical GAN architecture for each text-to-image synhesis group? | Semantic Enhancement GANs: DC-GANs, MC-GAN
Resolution Enhancement GANs: StackGANs, AttnGAN, HDGAN
Diversity Enhancement GANs: AC-GAN, TAC-GAN etc.
Motion Enhancement GAGs: T2S, T2V, StoryGAN | Text-to-image synthesis refers to computational methods which translate human written textual descriptions, in the form of keywords or sentences, into images with similar semantic meaning to the text. In earlier research, image synthesis relied mainly on word to image correlation analysis combined with supervised metho... | “ (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.” (2016) – Yann LeCun A picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communication, visual content, such as images, is a more compre... |
62 | What language do the agents talk in? | English | We introduce"Talk The Walk", the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a"guide"and a"tourist") that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are de... | 0pt0.03.03 * 0pt0.030.03 * 0pt0.030.03 We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target l... |
66 | How much better is performance of proposed method than state-of-the-art methods in experiments? | Accuracy of best proposed method KANE (LSTM+Concatenation) are 0.8011, 0.8592, 0.8605 compared to best state-of-the art method R-GCN + LR 0.7721, 0.8193, 0.8229 on three datasets respectively. | The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Many recent works have demonstrated the benefits of knowledge graph embedding on knowledge graph completion task, such as relation extraction. However, we observe that: 1) existing me... | In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs are usually encoded in the form of triples $(\textit {head entity}, relation,... |
67 | What stylistic features are used to detect drunk texts? | LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors, Spelling errors, Repeated characters, Capitalization, Length, Emoticon (Presence/Count), Sentiment Ratio. | Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a text was written when under the influence of alcohol. We experiment with tweets labeled using hashtags as distant supervision. Our classifier... | The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-... |
68 | What is the accuracy of the proposed technique? | 51.7 and 51.6 on 4th and 8th grade question sets with no curated knowledge. 47.5 and 48.0 on 4th and 8th grade question sets when both solvers are given the same knowledge | While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such ... | Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatically constructed open vocabulary (subject; predicate; object) s... |
70 | Which retrieval system was used for baselines? | The dataset comes with a ranked set of relevant documents. Hence the baselines do not use a retrieval system. | We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text. The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website... | Factoid Question Answering (QA) aims to extract answers, from an underlying knowledge source, to information seeking questions posed in natural language. Depending on the knowledge source available there are two main approaches for factoid QA. Structured sources, including Knowledge Bases (KBs) such as Freebase BIBREF1... |
71 | How much better was the BLSTM-CNN-CRF than the BLSTM-CRF? | Best BLSTM-CNN-CRF had F1 score 86.87 vs 86.69 of best BLSTM-CRF | In recent years, Vietnamese Named Entity Recognition (NER) systems have had a great breakthrough when using Deep Neural Network methods. This paper describes the primary errors of the state-of-the-art NER systems on Vietnamese language. After conducting experiments on BLSTM-CNN-CRF and BLSTM-CRF models with different w... | Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of persons, organizations, locations, expressions of times, quantities, monetary values... |
72 | What supplemental tasks are used for multitask learning? | Multitask learning is used for the task of predicting relevance of a comment on a different question to a given question, where the supplemental tasks are predicting relevance between the questions, and between the comment and the corresponding question | We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechan... | Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large collection of question-comment threads generated by these users. H... |
73 | How big is their model? | Proposed model has 1.16 million parameters and 11.04 MB. | Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term... | Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, given a sentence “I hated their service, but their food was gre... |
75 | How many emotions do they look at? | 9 | We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives available on Wattpad, annotated using Amazon Mechanical Turk. A ... | Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms. Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIBREF2. These datasets are often limited in ... |
79 | What is the performance of proposed model on entire DROP dataset? | The proposed model achieves EM 77,63 and F1 80,73 on the test and EM 76,95 and F1 80,25 on the dev | With models reaching human performance on many popular reading comprehension datasets in recent years, a new dataset, DROP, introduced questions that were expected to present a harder challenge for reading comprehension models. Among these new types of questions were "multi-span questions", questions whose answers cons... | The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance on the popular SQuAD dataset BIBREF0, and with much of the most popular reading compre... |
80 | How accurate is the aspect based sentiment classifier trained only using the XR loss? | BiLSTM-XR-Dev Estimation accuracy is 83.31 for SemEval-15 and 87.68 for SemEval-16.
BiLSTM-XR accuracy is 83.31 for SemEval-15 and 88.12 for SemEval-16.
| Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel applica... | Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manual annotations which are relatively hard to obtain at scale. An attractive... |
81 | What were the non-neural baselines used for the task? | The Lemming model in BIBREF17 | The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years' inflection tasks by examining trans... | While producing a sentence, humans combine various types of knowledge to produce fluent output—various shades of meaning are expressed through word selection and tone, while the language is made to conform to underlying structural rules via syntax and morphology. Native speakers are often quick to identify disfluency, ... |
83 | What are the models evaluated on? | They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA) | Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary inf... | Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read through a document to answer a question about informatio... |
84 | What is the results of multimodal compared to unimodal models? | Unimodal LSTM vs Best Multimodal (FCM)
- F score: 0.703 vs 0.704
- AUC: 0.732 vs 0.734
- Mean Accuracy: 68.3 vs 68.4 | In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimo... | Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that e... |
85 | What were their performance results? | On SearchSnippets dataset ACC 77.01%, NMI 62.94%, on StackOverflow dataset ACC 51.14%, NMI 49.08%, on Biomedical dataset ACC 43.00%, NMI 38.18% | Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep tex... | Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a r... |
89 | What is the state of the art? | POS and DP task: CONLL 2018
NER task: (no extensive work) Strong baselines CRF and BiLSTM-CRF
NLI task: mBERT or XLM (not clear from text) | Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models—in all languages except English—very limited. Aiming to addre... | Pretrained word representations have a long history in Natural Language Processing (NLP), from non-neural methods BIBREF0, BIBREF1, BIBREF2 to neural word embeddings BIBREF3, BIBREF4 and to contextualised representations BIBREF5, BIBREF6. Approaches shifted more recently from using these representations as an input to ... |
91 | What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains? | Tweets noisy nature, use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, short (length limited) text | Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions... | Microblog sentiment analysis; Twitter opinion mining |
92 | How are possible sentence transformations represented in dataset, as new sentences? | Yes, as new sentences. | COSTRA 1.0 is a dataset of Czech complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. ::: The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such ... | Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects. Studies of word embeddings range from word simil... |
94 | What semantic rules are proposed? | rules that compute polarity of words after POS tagging or parsing steps | This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for ... | Twitter sentiment classification have intensively researched in recent years BIBREF0 BIBREF1 . Different approaches were developed for Twitter sentiment classification by using machine learning such as Support Vector Machine (SVM) with rule-based features BIBREF2 and the combination of SVMs and Naive Bayes (NB) BIBREF3... |
96 | What is the performance of the model? | Experiment 1: ACC around 0.5 with 50% noise rate in worst case - clearly higher than baselines for all noise rates
Experiment 2: ACC on real noisy datasets: 0.7 on Movie, 0.79 on Laptop, 0.86 on Restaurant (clearly higher than baselines in almost all cases) | Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We prop... | It is well known that sentiment annotation or labeling is subjective BIBREF0. Annotators often have many disagreements. This is especially so for crowd-workers who are not well trained. That is why one always feels that there are many errors in an annotated dataset. In this paper, we study whether it is possible to bui... |
98 | Which of the two speech recognition models works better overall on CN-Celeb? | x-vector | Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. However, most publicly available datasets are collected under constrained environments, i.e., with little noise and limited channel variation... | Speaker recognition including identification and verification, aims to recognize claimed identities of speakers. After decades of research, performance of speaker recognition systems has been vastly improved, and the technique has been deployed to a wide range of practical applications. Nevertheless, the present speake... |
99 | How do the authors measure performance? | Accuracy across six datasets | We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly... | Deep neural network-based models are easy to overfit and result in losing their generalization due to limited size of training data. In order to address the issue, data augmentation methods are often applied to generate more training samples. Recent years have witnessed great success in applying data augmentation in th... |
100 | What learning paradigms do they cover in this survey? | Considering "What" and "How" separately versus jointly optimizing for both. | Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These trends point to NQG as a bellwether for NLP, about how human intelligence embodies the skills of curiosity and integration. We present a compreh... | Question Generation (QG) concerns the task of “automatically generating questions from various inputs such as raw text, database, or semantic representation" BIBREF0 . People have the ability to ask rich, creative, and revealing questions BIBREF1 ; e.g., asking Why did Gollum betray his master Frodo Baggins? after read... |
105 | How is the data annotated? | The data are self-reported by Twitter users and then verified by two human experts. | With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing te... | 0pt*0*0 0pt*0*0 0pt*0*0 0.95 1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State University, OH, USA [2]Ohio State University, Columbus... |
108 | What result from experiments suggest that natural language based agents are more robust? | Average reward across 5 seeds show that NLP representations are robust to changes in the environment as well task-nuisances | Recent advances in Reinforcement Learning have highlighted the difficulties in learning within complex high dimensional domains. We argue that one of the main reasons that current approaches do not perform well, is that the information is represented sub-optimally. A natural way to describe what we observe, is through ... | “The world of our experiences must be enormously simplified and generalized before it is possible to make a symbolic inventory of all our experiences of things and relations." (Edward Sapir, Language: An Introduction to the Study of Speech, 1921) Deep Learning based algorithms use neural networks in order to learn feat... |
110 | Which datasets are used in the paper? | Google N-grams
COHA
Moral Foundations Dictionary (MFD)
| We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people's moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embed... | People's moral sentiment—our feelings toward right or wrong—can change over time. For instance, the public's views toward slavery have shifted substantially over the past centuries BIBREF0. How society's moral views evolve has been a long-standing issue and a constant source of controversy subject to interpretations fr... |
112 | How much is proposed model better in perplexity and BLEU score than typical UMT models? | Perplexity of the best model is 65.58 compared to best baseline 105.79.
Bleu of the best model is 6.57 compared to best baseline 5.50. | Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems fro... | During thousands of years, millions of classical Chinese poems have been written. They contain ancient poets' emotions such as their appreciation for nature, desiring for freedom and concerns for their countries. Among various types of classical poetry, quatrain poems stand out. On the one hand, their aestheticism and ... |
114 | Do they train a different training method except from scheduled sampling? | Answer with content missing: (list missing)
Scheduled sampling: In our experiments, we found that models trained with scheduled sampling performed better (about 0.004 BLEU-4 on validation set) than the ones trained using teacher-forcing for the AVSD dataset. Hence, we use scheduled sampling for all the results we repo... | Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems. The Audio Visual Scene-aware Dialog (AVSD) challenge, organized as a track of the Dialog System Technology Challenge 7 (DSTC7), proposes a combined task, where a syst... | Deep neural networks have been successfully applied to several computer vision tasks such as image classification BIBREF0 , object detection BIBREF1 , video action classification BIBREF2 , etc. They have also been successfully applied to natural language processing tasks such as machine translation BIBREF3 , machine re... |
116 | How do they define upward and downward reasoning? | Upward reasoning is defined as going from one specific concept to a more general one. Downward reasoning is defined as the opposite, going from a general concept to one that is more specific. | Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still ... | Natural language inference (NLI), also known as recognizing textual entailment (RTE), has been proposed as a benchmark task for natural language understanding. Given a premise $P$ and a hypothesis $H$ , the task is to determine whether the premise semantically entails the hypothesis BIBREF0 . A number of recent works a... |
118 | Do they annotate their own dataset or use an existing one? | Use an existing one | Audiovisual synchronisation is the task of determining the time offset between speech audio and a video recording of the articulators. In child speech therapy, audio and ultrasound videos of the tongue are captured using instruments which rely on hardware to synchronise the two modalities at recording time. Hardware sy... | Ultrasound tongue imaging (UTI) is a non-invasive way of observing the vocal tract during speech production BIBREF0 . Instrumental speech therapy relies on capturing ultrasound videos of the patient's tongue simultaneously with their speech audio in order to provide a diagnosis, design treatments, and measure therapy p... |
120 | What web and user-generated NER datasets are used for the analysis? | MUC, CoNLL, ACE, OntoNotes, MSM, Ritter, UMBC | Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, featur... | Named entity recognition and classification (NERC, short NER), the task of recognising and assigning a class to mentions of proper names (named entities, NEs) in text, has attracted many years of research BIBREF0 , BIBREF1 , analyses BIBREF2 , starting from the first MUC challenge in 1995 BIBREF3 . Recognising entities... |
121 | Which unlabeled data do they pretrain with? | 1000 hours of WSJ audio data | We explore unsupervised pre-training for speech recognition by learning representations of raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting representations are then used to improve acoustic model training. We pre-train a simple multi-layer convolutional neural network optimized v... | Current state of the art models for speech recognition require large amounts of transcribed audio data to attain good performance BIBREF1 . Recently, pre-training of neural networks has emerged as an effective technique for settings where labeled data is scarce. The key idea is to learn general representations in a set... |
123 | How big are the datasets? | In-house dataset consists of 3716 documents
ACE05 dataset consists of 1635 documents | Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challengi... | Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities like persons, organizations, geo-political entities, locations, and events. It provides useful information for many NLP applications such as knowledge base construction, text mi... |
125 | What baselines did they compare their model with? | the baseline where path generation uses a standard sequence-to-sequence model augmented with attention mechanism and path verification uses depth-first search | We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performan... | Enabling robots to follow navigation instructions in natural language can facilitate human-robot interaction across a variety of applications. For instance, within the service robotics domain, robots can follow navigation instructions to help with mobile manipulation BIBREF0 and delivery tasks BIBREF1 . Interpreting na... |
126 | What additional features are proposed for future work? | distinguishing between clinically positive and negative phenomena within each risk factor domain and accounting for structured data collected on the target cohort | Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health recor... | Psychotic disorders typically emerge in late adolescence or early adulthood BIBREF0 , BIBREF1 and affect approximately 2.5-4% of the population BIBREF2 , BIBREF3 , making them one of the leading causes of disability worldwide BIBREF4 . A substantial proportion of psychiatric inpatients are readmitted after discharge BI... |
127 | How is morphology knowledge implemented in the method? | A BPE model is applied to the stem after morpheme segmentation. | Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years. However, in consideration of efficiency, a limited-size vocabulary that only contains the top-N highest frequency words are employed for model training, which leads to many rare and unknown words. It is rat... | Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years for many language pairs BIBREF0, BIBREF1, BIBREF2. However, in consideration of time cost and space capacity, the NMT model generally employs a limited-size vocabulary that only contains the top-N highest fr... |
129 | How is the performance on the task evaluated? | Comparison of test accuracies of neural network models on an inflection task and qualitative analysis of the errors | How does knowledge of one language's morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target la... | A widely agreed-on fact in language acquisition research is that learning of a second language (L2) is influenced by a learner's native language (L1) BIBREF0, BIBREF1. A language's morphosyntax seems to be no exception to this rule BIBREF2, but the exact nature of this influence remains unknown. For instance, it is unc... |
131 | what datasets were used? | IWSLT14 German-English, IWSLT14 Turkish-English, WMT14 English-German | Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework. From the optimization perspective, residual connections are adopted to improve learning performance for both encoder and decoder in most of these deep architectures... | Neural machine translation (NMT) is a challenging task that attracts lots of attention in recent years. Starting from the encoder-decoder framework BIBREF0 , NMT starts to show promising results in many language pairs. The evolving structures of NMT models in recent years have made them achieve higher scores and become... |
133 | How do they obtain human judgements? | Using crowdsourcing | Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on"distant reading"by algorithmically discoveri... | Literary critics form interpretations of meaning in works of literature. Building computational models that can help form and test these interpretations is a fundamental goal of digital humanities research BIBREF0 . Within natural language processing, most previous work that engages with literature relies on “distant r... |
134 | Does this approach perform better in the multi-domain or single-domain setting? | single-domain setting | Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined ... | A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system BIBREF7 . For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided based on the dialogue state accordi... |
135 | How many samples did they generate for the artificial language? | 70,000 | Can neural nets learn logic? We approach this classic question with current methods, and demonstrate that recurrent neural networks can learn to recognize first order logical entailment relations between expressions. We define an artificial language in first-order predicate logic, generate a large dataset of sample 'se... | State-of-the-art models for almost all popular natural language processing tasks are based on deep neural networks, trained on massive amounts of data. A key question that has been raised in many different forms is to what extent these models have learned the compositional generalizations that characterize language, an... |
137 | Why does not the approach from English work on other languages? | Because, unlike other languages, English does not mark grammatical genders | Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present... | One of the biggest challenges faced by modern natural language processing (NLP) systems is the inadvertent replication or amplification of societal biases. This is because NLP systems depend on language corpora, which are inherently “not objective; they are creations of human design” BIBREF0 . One type of societal bias... |
141 | How many layers does their system have? | 4 layers | For a news content distribution platform like Dailyhunt, Named Entity Recognition is a pivotal task for building better user recommendation and notification algorithms. Apart from identifying names, locations, organisations from the news for 13+ Indian languages and use them in algorithms, we also need to identify n-gr... | Named-Entity-Recognition(NER) approaches can be categorised broadly in three types. Detecting NER with predefined dictionaries and rulesBIBREF2, with some statistical approachesBIBREF3 and with deep learning approachesBIBREF4. Stanford CoreNLP NER is a widely used baseline for many applications BIBREF5. Authors have us... |
143 | What context modelling methods are evaluated? | Concat
Turn
Gate
Action Copy
Tree Copy
SQL Attn
Concat + Action Copy
Concat + Tree Copy
Concat + SQL Attn
Turn + Action Copy
Turn + Tree Copy
Turn + SQL Attn
Turn + SQL Attn + Action Copy | Recently semantic parsing in context has received a considerable attention, which is challenging since there are complex contextual phenomena. Previous works verified their proposed methods in limited scenarios, which motivates us to conduct an exploratory study on context modeling methods under real-world semantic par... | Semantic parsing, which translates a natural language sentence into its corresponding executable logic form (e.g. Structured Query Language, SQL), relieves users from the burden of learning techniques behind the logic form. The majority of previous studies on semantic parsing assume that queries are context-independent... |
145 | Is the baseline a non-heirarchical model like BERT? | There were hierarchical and non-hierarchical baselines; BERT was one of those baselines | Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \emph{inaccurate} labels is challenging. Inspired by the recent work... | Automatic document summarization is the task of rewriting a document into its shorter form while still retaining its important content. Over the years, many paradigms for document summarization have been explored (see Nenkova:McKeown:2011 for an overview). The most popular two among them are extractive approaches and a... |
147 | How better are results compared to baseline models? | F1 score of best authors' model is 55.98 compared to BiLSTM and FastText that have F1 score slighlty higher than 46.61. | Research in the social sciences and psychology has shown that the persuasiveness of an argument depends not only the language employed, but also on attributes of the source/communicator, the audience, and the appropriateness and strength of the argument's claims given the pragmatic and discourse context of the argument... | Previous work in the social sciences and psychology has shown that the impact and persuasive power of an argument depends not only on the language employed, but also on the credibility and character of the communicator (i.e. ethos) BIBREF0, BIBREF1, BIBREF2; the traits and prior beliefs of the audience BIBREF3, BIBREF4... |
148 | How big is dataset used for training/testing? | 4,261 days for France and 4,748 for the UK | While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevant information they may contain. In this work, openly accessible daily weather reports from France and the United-Kingdom are leveraged to pr... | Whether it is in the field of energy, finance or meteorology, accurately predicting the behavior of time series is nowadays of paramount importance for optimal decision making or profit. While the field of time series forecasting is extremely prolific from a research point-of-view, up to now it has narrowed its efforts... |
149 | What are the state-of-the-art systems? | For sentiment analysis UWB, INF-UFRGS-OPINION-MINING, LitisMind, pkudblab and SVM + n-grams + sentiment and for emotion analysis MaxEnt, SVM, LSTM, BiLSTM and CNN | In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion pre... | The emergence of social media sites with limited character constraint has ushered in a new style of communication. Twitter users within 280 characters per tweet share meaningful and informative messages. These short messages have a powerful impact on how we perceive and interact with other human beings. Their compact n... |
151 | What is the size of their collected dataset? | 3347 unique utterances | Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Ex... | Understanding passenger intents and extracting relevant slots are important building blocks towards developing a contextual dialogue system responsible for handling certain vehicle-passenger interactions in autonomous vehicles (AV). When the passengers give instructions to AMIE (Automated-vehicle Multimodal In-cabin Ex... |
152 | What kind of features are used by the HMM models, and how interpretable are those? | A continuous emission HMM uses the hidden states of a 2-layer LSTM as features and a discrete emission HMM uses data as features.
The interpretability of the model is shown in Figure 2. | As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks (RNNs), state of the art models in speech re... | Following the recent progress in deep learning, researchers and practitioners of machine learning are recognizing the importance of understanding and interpreting what goes on inside these black box models. Recurrent neural networks have recently revolutionized speech recognition and translation, and these powerful mod... |
153 | what was their system's f1 performance? | Proposed model achieves 0.86, 0.924, 0.71 F1 score on SR, HATE, HAR datasets respectively. | We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on thr... | The increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms typically forbid hateful and haras... |
155 | How was quality measured? | Inter-annotator agreement, comparison against expert annotation, agreement with PropBank Data annotations. | Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-... | Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are captured by natural language question... |
156 | What data were they used to train the multilingual encoder? | WMT 2014 En-Fr parallel corpus | Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can transla... | Transfer learning has been shown to work well in Computer Vision where pre-trained components from a model trained on ImageNet BIBREF0 are used to initialize models for other tasks BIBREF1 . In most cases, the other tasks are related to and share architectural components with the ImageNet task, enabling the use of such... |
157 | From when are many VQA datasets collected? | late 2014 | In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determi... | In open-ended visual question answering (VQA) an algorithm must produce answers to arbitrary text-based questions about images BIBREF0 , BIBREF1 . VQA is an exciting computer vision problem that requires a system to be capable of many tasks. Truly solving VQA would be a milestone in artificial intelligence, and would s... |
158 | Does proposed end-to-end approach learn in reinforcement or supervised learning manner? | supervised learning | In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a task, which in turn is used to synthesize specific motion controllers at run-time. This multimodal approach enables generalization to a wide v... | A significant challenge when designing robots to operate in the real world lies in the generation of control policies that can adapt to changing environments. Programming such policies is a labor and time-consuming process which requires substantial technical expertise. Imitation learning BIBREF0, is an appealing metho... |
163 | How big dataset is used for training this system? | For the question generation model 15,000 images with 75,000 questions. For the chatbot model, around 460k utterances over 230k dialogues. | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapie... | Increases in life expectancy in the last century have resulted in a large number of people living to old ages and will result in a double number of dementia cases by the middle of the century BIBREF0BIBREF1. The most common form of dementia is Alzheimer disease which contributes to 60–70% of cases BIBREF2. Research foc... |
164 | How do they obtain word lattices from words? | By considering words as vertices and generating directed edges between neighboring words within a sentence | Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to u... | Short text matching plays a critical role in many natural language processing tasks, such as question answering, information retrieval, and so on. However, matching text sequences for Chinese or similar languages often suffers from word segmentation, where there are often no perfect Chinese word segmentation tools that... |
167 | How better is proposed method than baselines perpexity wise? | Perplexity of proposed MEED model is 19.795 vs 19.913 of next best result on test set. | Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing. Some of the recent work aims at incorporating affect information into sequence-to-sequence neural dialog modeling, making the response emotionally richer, while others use hand-crafted rules to determin... | Recent development in neural language modeling has generated significant excitement in the open-domain dialog generation community. The success of sequence-to-sequence learning BIBREF0, BIBREF1 in the field of neural machine translation has inspired researchers to apply the recurrent neural network (RNN) encoder-decode... |
169 | Who manually annotated the semantic roles for the set of learner texts? | Authors | This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language. We first manually annotate the semantic roles for a set of learner texts to derive a gold standard for automatic SRL. Based on the new data, we then evaluate three off-t... | A learner language (interlanguage) is an idiolect developed by a learner of a second or foreign language which may preserve some features of his/her first language. Previously, encouraging results of automatically building the syntactic analysis of learner languages were reported BIBREF0 , but it is still unknown how s... |
170 | How do they obtain region descriptions and object annotations? | they are available in the Visual Genome dataset | A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image. Current approaches with this capability rely on supervised learning and human annotated groundings to train attention mechanisms inside the VQA architecture. Unfortunately, obtaining human annota... | We are interested in the problem of visual question answering (VQA), where an algorithm is presented with an image and a question that is formulated in natural language and relates to the contents of the image. The goal of this task is to get the algorithm to correctly answer the question. The VQA task has recently rec... |
171 | Which training dataset allowed for the best generalization to benchmark sets? | MultiNLI | Neural network models have been very successful in natural language inference, with the best models reaching 90% accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on a natural language inference dataset drawn from one benchmark fail... | Natural Language Inference (NLI) has attracted considerable interest in the NLP community and, recently, a large number of neural network-based systems have been proposed to deal with the task. One can attempt a rough categorization of these systems into: a) sentence encoding systems, and b) other neural network system... |
172 | What dataset do they use? | They used Wiki Vietnamese language and Vietnamese newspapers to pretrain embeddings and dataset provided in HSD task to train model (details not mentioned in paper). | Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking differen... | Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on... |
178 | Do the use word embeddings alone or they replace some previous features of the model with word embeddings? | They use it as addition to previous model - they add new edge between words if word embeddings are similar. | Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios. Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text. Here we investigate whether... | The ability to construct complex and diverse linguistic structures is one of the main features that set us apart from all other species. Despite its ubiquity, some language aspects remain unknown. Topics such as language origin and evolution have been studied by researchers from diverse disciplines, including Linguisti... |
179 | How many natural language explanations are human-written? | Totally 6980 validation and test image-sentence pairs have been corrected. | The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in which SNLI-VE has been assembled (via combining parts of two related datasets) gives rise to a large number of errors in the labels of this c... | Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to each pair of (p... |
180 | What is the dataset used as input to the Word2Vec algorithm? | Italian Wikipedia and Google News extraction producing final vocabulary of 618224 words | Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In ... | In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this r... |
182 | What methodology is used to compensate for limited labelled data? | Influential tweeters ( who they define as individuals certain to have a classifiable sentiment regarding the topic at hand) is used to label tweets in bulk in the absence of manually-labeled tweets. | While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences. On the sub-population of Twitt... | Much prior work has been done at the intersection of climate change and Twitter, such as tracking climate change sentiment over time BIBREF2 , finding correlations between Twitter climate change sentiment and seasonal effects BIBREF3 , and clustering Twitter users based on climate mentalities using network analysis BIB... |
183 | What are the baseline state of the art models? | Stanford NER, BiLSTM+CRF, LSTM+CNN+CRF, T-NER and BiLSTM+CNN+Co-Attention | Named Entity Recognition (NER) from social media posts is a challenging task. User generated content which forms the nature of social media, is noisy and contains grammatical and linguistic errors. This noisy content makes it much harder for tasks such as named entity recognition. However some applications like automat... | A common social media delivery system such as Twitter supports various media types like video, image and text. This media allows users to share their short posts called Tweets. Users are able to share their tweets with other users that are usually following the source user. Hovewer there are rules to protect the privac... |
187 | How do they extract causality from text? | They identify documents that contain the unigrams 'caused', 'causing', or 'causes' | Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium. Despite the increasing importance of social media, much remains unknown about causal sta... | Social media and online social networks now provide vast amounts of data on human online discourse and other activities BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . With so much communication taking place online and with social media being capable of hosting powerful misinformation campaigns BI... |
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