Vivek Raghavan


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Corpus for Automatic Structuring of Legal Documents
Prathamesh Kalamkar | Aman Tiwari | Astha Agarwal | Saurabh Karn | Smita Gupta | Vivek Raghavan | Ashutosh Modi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In populous countries, pending legal cases have been growing exponentially. There is a need for developing techniques for processing and organizing legal documents. In this paper, we introduce a new corpus for structuring legal documents. In particular, we introduce a corpus of legal judgment documents in English that are segmented into topical and coherent parts. Each of these parts is annotated with a label coming from a list of pre-defined Rhetorical Roles. We develop baseline models for automatically predicting rhetorical roles in a legal document based on the annotated corpus. Further, we show the application of rhetorical roles to improve performance on the tasks of summarization and legal judgment prediction. We release the corpus and baseline model code along with the paper.

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Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
Gowtham Ramesh | Sumanth Doddapaneni | Aravinth Bheemaraj | Mayank Jobanputra | Raghavan AK | Ajitesh Sharma | Sujit Sahoo | Harshita Diddee | Mahalakshmi J | Divyanshu Kakwani | Navneet Kumar | Aswin Pradeep | Srihari Nagaraj | Kumar Deepak | Vivek Raghavan | Anoop Kunchukuttan | Pratyush Kumar | Mitesh Shantadevi Khapra
Transactions of the Association for Computational Linguistics, Volume 10

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly available parallel corpora, and additionally mine 37.4 million sentence pairs from the Web, resulting in a 4× increase. We mine the parallel sentences from the Web by combining many corpora, tools, and methods: (a) Web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at Samanantar and we hope they will help advance research in NMT and multilingual NLP for Indic languages.