Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages

TL;DR

Samanantar is the largest publicly available parallel corpus collection for 11 Indic languages, significantly expanding resources for machine translation and multilingual NLP through web mining, human validation, and multilingual modeling.

Contribution

It introduces a vast, high-quality parallel corpus for Indic languages, created by combining existing data and novel web mining techniques, enabling improved multilingual translation models.

Findings

Multilingual NMT models trained on Samanantar outperform existing baselines.

The corpus covers 49.7 million sentence pairs across 11 languages.

High-quality parallel sentences validated through human evaluation.

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 4x 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 https://ai4bharat.iitm.ac.in/samanantar and we hope they will help advance research in NMT and multilingual NLP for Indic languages.