MC$^2$: Towards Transparent and Culturally-Aware NLP for Minority Languages in China
This addresses the accessibility challenge for low-resource minority languages in China, though it is incremental as it builds on existing corpus construction efforts.
The authors tackled the problem of limited pre-training data for minority languages in China by creating MC$^2$, the largest open-source multilingual corpus for Tibetan, Uyghur, Kazakh, and Mongolian, focusing on underrepresented writing systems and prioritizing data quality and diversity.
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 MC$^2$, a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus of its kind so far. MC$^2$ 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 MC$^2$, 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 MC$^2$ corpus and related models are made public to the community.