NE-BERT: A Multilingual Language Model for Nine Northeast Indian Languages

Abstract:Large pretrained language models have demonstrated remarkable capabilities across diverse languages, yet critically underrepresented low-resource languages remain marginalized. We present NE-BERT, a domain-specific multilingual encoder model trained on approximately 8.3 million sentences spanning 9 Northeast Indian languages and 2 anchor languages (Hindi, English), a linguistically diverse region with minimal representation in existing multilingual models. By employing weighted data sampling and a custom SentencePiece Unigram tokenizer, NE-BERT outperforms IndicBERT-V2 and MuRIL across all 9 Northeast Indian languages, achieving 15.97X and 7.64X lower average perplexity respectively, with 1.50X better tokenization fertility than mBERT. We address critical vocabulary fragmentation issues in extremely low-resource languages such as Pnar (1,002 sentences) and Kokborok (2,463 sentences) through aggressive upsampling strategies. Downstream evaluation on part-of-speech tagging validates practical utility on three Northeast Indian languages. We release NE-BERT, test sets, and training corpus under CC-BY-4.0 to support NLP research and digital inclusion for Northeast Indian communities.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.18094 [cs.CL]

(or arXiv:2608.18094v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.18094 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.18653/v1/2026.loreslm-1.1 Focus to learn more DOI(s) linking to related resources

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