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

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

14 Nov 2023arXiv:2311.08348archive 2025-07-28

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

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.

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read_data luciusssss/mc2_corpus/scripts/culturax_to_json.py official repository unverified CC0-1.0 (permissive) · d9945aa058cbefe7 · report
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convert_dataset_into_prompt_translation luciusssss/mlic-eval/infer.py community (archive-listed) unverified MIT (permissive) · 6e02320e67d19916 · report
remove_special_tokens luciusssss/mlic-eval/infer.py community (archive-listed) unverified MIT (permissive) · dc6235427befec3f · report

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