Update README.md

README.md

@@ -301,12 +301,12 @@ configs:
   - split: urd
     path: unalignable/urd-*
 tags:
-- data-mining
-- document-alignment
 - parallel-corpus
+- document-alignment
+- machine-translation
 ---
 
-# Pralekha: An Indic Document Alignment Evaluation Benchmark
+# Pralekha: Cross-Lingual Document Alignment for Indic Languages
 
 <div style="display: flex; gap: 10px;">
   <a href="https://arxiv.org/abs/2411.19096">
@@ -323,41 +323,75 @@ tags:
   </a>
 </div>
 
-**PRALEKHA** is a large-scale parallel document dataset for evaluating cross-lingual document alignment techniques across **11 Indic languages** and English. It comprises over **3 million** document pairs, with **1.5 million** being English-centric.  
+**Pralekha** is a large-scale parallel document dataset for Cross-Lingual Document Alignment (CLDA) and Machine Translation (MT) across **11 Indic languages** and English. It comprises over **3 million** document pairs, with **1.5 million** being English-centric.  
 
 ---
 
 ## Dataset Description
 
-**PRALEKHA** covers 12 languages—Bengali (`ben`), Gujarati (`guj`), Hindi (`hin`), Kannada (`kan`), Malayalam (`mal`), Marathi (`mar`), Odia (`ori`), Punjabi (`pan`), Tamil (`tam`), Telugu (`tel`), Urdu (`urd`), and English (`eng`). It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: **news bulletins** and **podcast scripts**, offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
+**Pralekha** covers 12 languages—Bengali (`ben`), Gujarati (`guj`), Hindi (`hin`), Kannada (`kan`), Malayalam (`mal`), Marathi (`mar`), Odia (`ori`), Punjabi (`pan`), Tamil (`tam`), Telugu (`tel`), Urdu (`urd`), and English (`eng`). It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: **News Bulletins** ([Indian Press Information Bureau (PIB)](https://pib.gov.in)) and **Podcast Scripts** ([Mann Ki Baat](https://www.pmindia.gov.in/en/mann-ki-baat)), offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
+
+While this accounts for `alignable` (parallel) documents, In real-world scenarios, multilingual corpora often include `unalignable` documents. To simulate this for CLDA evaluation, we sample `unalignable` documents from [Sangraha Unverified](https://huggingface.co/datasets/ai4bharat/sangraha/viewer/unverified), selecting 50% of Pralekha’s size to maintain a 1:2 ratio of `unalignable` to `alignable` documents. 
+
+For Machine Translation (MT) tasks, we first randomly sample 1,000 documents per English-Indic language pair, ensuring a good distribution of varying document lengths. After excluding these sampled documents, we use the remaining documents for training document-level machine translation models.
 
-The dataset has a **1:2 ratio of aligned to unaligned document pairs**, making it ideal for benchmarking cross-lingual document alignment techniques.
 
-### Data Fields
+## Data Fields
 
-Each data sample includes:
+### Alignable & Unalignable Set:
 
-- **`n_id`:** Unique identifier for aligned document pairs.
+- **`n_id`:** Unique identifier for `alignable` document pairs (Random `n_id`'s are assigned for the `unalignable` set.)
 - **`doc_id`:** Unique identifier for individual documents.
-- **`lang`:** Language of the document (ISO-3 code).
+- **`lang`:** Language of the document (ISO 639-3 code).
 - **`text`:** The textual content of the document.
 
-### Data Sources
+### Train, Dev & Test Set:
+
+- **`src_lang`:** Source Language (eng)
+- **`src_text`:** Source Language Text
+- **`tgt_lang`:** Target Language ((ISO 639-3 code)
+- **`tgt_text`:** Target Language Text
+
+
+## Usage
+
+You can load specific **subsets** and **splits** from this dataset using the `datasets` library.
+
+### Load an entire subset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>")
+# <subset> = alignable, unalignable, train, dev & test.
+```
+
+### Load a specific split within a subset
 
-1. **News Bulletins:** Data was custom-scraped from the [Indian Press Information Bureau (PIB)](https://pib.gov.in) website. Documents were aligned by matching bulletin IDs, which interlink bulletins across languages.
-2. **Podcast Scripts:** Data was sourced from [Mann Ki Baat](https://www.pmindia.gov.in/en/mann-ki-baat), a radio program hosted by the Indian Prime Minister. This program, originally spoken in Hindi, was manually transcribed and translated into various Indian languages.
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>/<lang>")
+# <subset> = alignable, unalignable ; <lang> = ben, eng, guj, hin, kan, mal, mar, ori, pan, tam, tel, urd.
+```
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>/eng_<lang>")
+# <subset> = train, dev & test ; <lang> = ben, guj, hin, kan, mal, mar, ori, pan, tam, tel, urd.
+```
 
-### Dataset Size Statistics
+## Data Size Statistics
 
 | Split         | Number of Documents | Size (bytes)       |
 |---------------|---------------------|--------------------|
-| **Aligned**   | 1,566,404           | 10,274,361,211     |
-| **Unaligned** | 783,197             | 4,466,506,637      |
+| **Alignable**   | 1,566,404           | 10,274,361,211     |
+| **Unalignable** | 783,197             | 4,466,506,637      |
 | **Total**     | 2,349,601           | 14,740,867,848     |
 
-### Language-wise Statistics
+## Language-wise Statistics
 
-| Language (`ISO-3`) | Aligned Documents | Unaligned Documents | Total Documents |
+| Language (`ISO-3`) | Alignable Documents | Unalignable Documents | Total Documents |
 |---------------------|-------------------|---------------------|-----------------|
 | Bengali (`ben`)     | 95,813            | 47,906              | 143,719         |
 | English (`eng`)     | 298,111           | 149,055             | 447,166         |
@@ -374,22 +408,15 @@ Each data sample includes:
 
 ---
 
-# Usage
-
-You can use the following commands to download and explore the dataset:
-
-## Downloading the Entire Dataset
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("ai4bharat/pralekha")
+# Citation
+If you use Pralekha in your work, please cite us:
 ```
-## Downloading a Specific Split
-``` python
-from datasets import load_dataset
-
-dataset = load_dataset("ai4bharat/pralekha", split="<split_name>")
-# For example: dataset = load_dataset("ai4bharat/pralekha", split="aligned")
+@article{suryanarayanan2024pralekha,
+  title={Pralekha: An Indic Document Alignment Evaluation Benchmark},
+  author={Suryanarayanan, Sanjay and Song, Haiyue and Khan, Mohammed Safi Ur Rahman and Kunchukuttan, Anoop and Khapra, Mitesh M and Dabre, Raj},
+  journal={arXiv preprint arXiv:2411.19096},
+  year={2024}
+}
 ```
 ---
 
@@ -408,4 +435,4 @@ For any questions or feedback, please contact:
 - Haiyue Song ([haiyue.song@nict.go.jp](mailto:haiyue.song@nict.go.jp))  
 - Mohammed Safi Ur Rahman Khan ([safikhan2000@gmail.com](mailto:safikhan2000@gmail.com))  
 
-Please get in touch with us for any copyright concerns.
+Please get in touch with us for any copyright concerns.