MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China

Chen Zhang, Mingxu Tao, Quzhe Huang, Jiuheng Lin, Zhibin Chen, Yansong Feng


Abstract
Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China. This limitation stems from the scarcity of available pre-training data. To address this accessibility challenge, we present MC2, a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus of its kind so far. MC2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian. Notably, we focus on the less common writing systems of Kazakh and Mongolian, i.e., Kazakh Arabic script and traditional Mongolian script, respectively, which have been long neglected in previous corpus construction efforts. Recognizing the prevalence of language contamination within existing corpora, we adopt a quality-centric solution for collecting MC2, prioritizing accuracy while enhancing diversity. Furthermore, we underscore the importance of attending to the multiplicity of writing systems, which is closely related to the cultural awareness of the resulting models. The MC2 corpus and related models are made public to the community.
Anthology ID:
2024.acl-long.479
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8832–8850
Language:
URL:
https://aclanthology.org/2024.acl-long.479
DOI:
Bibkey:
Cite (ACL):
Chen Zhang, Mingxu Tao, Quzhe Huang, Jiuheng Lin, Zhibin Chen, and Yansong Feng. 2024. MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8832–8850, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China (Zhang et al., ACL 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.479.pdf