Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2104.05596 v4 pith:O2GKCIJP submitted 2021-04-12 cs.CL

classification cs.CL
keywords corporalanguagesparallelindicpairssamanantarsentencesavailable
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Graph-Assisted Culturally Adaptable Idiomatic Translation for Indic Languages

    cs.CL 2025-05 conditional novelty 5.0 of 10

    IdiomCE uses a graph neural network trained on synthetic cultural-element similarity edges to retrieve target-language idioms for English-to-Indic and inter-Indic translation, improving GPT-4o-based quality scores ove...

  2. Beyond Specialization: Benchmarking LLMs for Transliteration of Indian Languages

    cs.CL 2025-05 conditional novelty 5.0 of 10

    GPT-4.5 and fine-tuned GPT-4o outperform the specialized IndicXlit model on most word-level Roman-to-Indic transliteration benchmarks across ten Indian languages.

Pith tools