Beyond Multilinguality: Typological Limitations in Multilingual Models for Meitei Language

AbstractWe present MeiteiRoBERTa, the first publicly available monolingual RoBERTa-based language model for Meitei (Manipuri), a low-resource language spoken by over 1.8 million people in Northeast India. Trained from scratch on 76 million words of Meitei text in Bengali script, our model achieves a perplexity of 65.89, representing a 5.2× improvement over multilingual baselines BERT (341.56) and MuRIL (355.65). Through comprehensive evaluation on perplexity, tokenization efficiency, and semantic representation quality, we demonstrate that domain-specific pre training significantly outperforms general-purpose multilingual models for low-resource languages. Our model exhibits superior semantic understanding with 0.769 similarity separation compared to 0.035 for mBERT and near-zero for MuRIL, despite MuRIL’s better tokenization efficiency (fertility: 3.29 vs. 4.65). We publicly release the model, training code, and datasets to accelerate NLP research for Meitei and other underrepresented Northeast Indian languages