We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 46.9 million sentence pairs between English and 11 Indic languages (from two language families). In particular, we compile 12.4 million sentence pairs from existing, publicly-available parallel corpora, and we additionally mine 34.6 million sentence pairs from the web, resulting in a 2.8X increase in publicly available sentence pairs. We mine the parallel sentences from the web by combining many corpora, tools, and methods. In particular, we use (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 language pairs. Further, we extracted 82.7 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 and compared with other baselines and previously reported results on publicly available benchmarks. Our models outperform existing models on these benchmarks, establishing the utility of Samanantar. Our data (https://indicnlp.ai4bharat.org/samanantar) and models (https://github.com/AI4Bharat/IndicTrans) will be available publicly and we hope they will help advance research in Indic NMT and multilingual NLP for Indic languages.
翻译:特别是,我们用(a) 网络绘制的单一语言Corbora,(b) 文件OCR,从扫描文件中提取判决(c) 用于协调判决的多语言代表模式,以及(d) 近距离的邻居搜索大量判决汇编。 人类对新开采的Corbora样本的评估证实,11对语言的平行判决质量很高。 此外,我们用英语作为参考语言,从所有55种印度语平行模型中提取了8 270万张判决配对(我们用网络绘制的单语Corora,(b) 文件OCR),从扫描文件中提取判决(c) 用于协调判决的多语言代表模式,以及(d) 近距离的邻居搜索大量判决汇编。