Part-of-speech Tagging for Extremely Low-resource Indian Languages

Modern natural language processing (NLP) systems thrive when given access to large datasets. However, a large fraction of the world's languages are not privy to such benefits due to sparse documentation and inadequate digital representation. This is especially true for Indian regional languages. As a first step towards expanding the reach of NLP technologies to extremely low-resource Indian languages, we present a new parallel part-of-speech (POS) evaluation dataset for Angika, Magahi, Bhojpuri and Hindi. Angika, Magahi, Bhojpuri, along with the more well-known Hindi, are all languages spoken in the Indian states of Bihar, Jharkhand and West Bengal. Ours is notably the first NLP resource, even for a shallow NLP task like POS-tagging, for Angika. We establish POS-tagging baselines using state-ofthe-art multilingual pretrained language models (PLMs) finetuned on Hindi data, and show zeroshot evaluations on the other three languages. While all four languages use the same Devanagari script, pretrained tokenizers underperform in zero-shot on the three languages. We propose a simple look-back fix to address the tokenization challenge yielding F1-score improvements of up to 8% on Angika, and show how it comes very close to an oracle setting when the underlying Hindi word is known (and can be accurately tokenized).