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).- Anthology ID:
- 2024.findings-acl.857
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14422–14431
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.857
- DOI:
- Cite (ACL):
- Sanjeev Kumar, Preethi Jyothi, and Pushpak Bhattacharyya. 2024. Part-of-speech Tagging for Extremely Low-resource Indian Languages. In Findings of the Association for Computational Linguistics ACL 2024, pages 14422–14431, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Part-of-speech Tagging for Extremely Low-resource Indian Languages (Kumar et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2024.findings-acl.857.pdf