Exploring Cross-Lingual Knowledge Transfer via Transliteration-Based MLM Fine-Tuning for Critically Low-resource Chakma Language

• URL: http://arxiv.org/abs/2510.09032v1
• Date: Fri, 10 Oct 2025 06:07:14 GMT
• Title: Exploring Cross-Lingual Knowledge Transfer via Transliteration-Based MLM Fine-Tuning for Critically Low-resource Chakma Language
• Authors: Adity Khisa, Nusrat Jahan Lia, Tasnim Mahfuz Nafis, Zarif Masud, Tanzir Pial, Shebuti Rayana, Ahmedul Kabir,
• Abstract summary: As an Indo-Aryan language with limited available data, Chakma remains largely underrepresented in language models.<n>We introduce a novel corpus of contextually coherent Bangla-transliterated Chakma, curated from Chakma literature, and validated by native speakers.<n>Experiments show that fine-tuned multilingual models outperform their pre-trained counterparts when adapted to Bangla-transliterated Chakma.
• Score: 1.4206084598312039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an Indo-Aryan language with limited available data, Chakma remains largely underrepresented in language models. In this work, we introduce a novel corpus of contextually coherent Bangla-transliterated Chakma, curated from Chakma literature, and validated by native speakers. Using this dataset, we fine-tune six encoder-based multilingual and regional transformer models (mBERT, XLM-RoBERTa, DistilBERT, DeBERTaV3, BanglaBERT, and IndicBERT) on masked language modeling (MLM) tasks. Our experiments show that fine-tuned multilingual models outperform their pre-trained counterparts when adapted to Bangla-transliterated Chakma, achieving up to 73.54% token accuracy and a perplexity as low as 2.90. Our analysis further highlights the impact of data quality on model performance and shows the limitations of OCR pipelines for morphologically rich Indic scripts. Our research demonstrates that Bangla-transliterated Chakma can be very effective for transfer learning for Chakma language, and we release our manually validated monolingual dataset to encourage further research on multilingual language modeling for low-resource languages.

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