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  1. Home
  2. Indian Institute of Technology Madras
  3. Publication2
  4. Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
 
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Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages

Date Issued
09-02-2022
Author(s)
Ramesh, Gowtham
Doddapaneni, Sumanth
Bheemaraj, Aravinth
Jobanputra, Mayank
Raghavan, A. K.
Sharma, Ajitesh
Sahoo, Sujit
Diddee, Harshita
Mahalakshmi, J.
Kakwani, Divyanshu
Kumar, Navneet
Pradeep, Aswin
Nagaraj, Srihari
Deepak, Kumar
Raghavan, Vivek
Kunchukuttan, Anoop
Kumar, Pratyush
Shantadevi, Mitesh
Khapra,
DOI
10.1162/tacl_a_00452
Abstract
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.
Volume
10
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