@inproceedings{kumar-etal-2024-part,
    title = "Part-of-speech Tagging for Extremely Low-resource {I}ndian Languages",
    author = "Kumar, Sanjeev  and
      Jyothi, Preethi  and
      Bhattacharyya, Pushpak",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-acl.857/",
    doi = "10.18653/v1/2024.findings-acl.857",
    pages = "14422--14431",
    abstract = "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-of-the-art multilingual pretrained language models (PLMs) finetuned on Hindi data, and show zero-shot 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)."
}