@inproceedings{yadav-etal-2026-maibert,
    title = "{M}ai{BERT}: A Pre-training Corpus and Language Model for Low-Resourced {M}aithili Language",
    author = "Yadav, Sumit  and
      Yadav, Raju Kumar  and
      Maskey, Utsav  and
      Kashyap, Gautam Siddharth  and
      Gautam, Ganesh  and
      Naseem, Usman",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.loreslm-1.38/",
    pages = "444--452",
    ISBN = "979-8-89176-377-7",
    abstract = "Natural Language Understanding (NLU) for low-resource languages remains a major challenge in NLP due to the scarcity of high-quality data and language-specific models. Maithili, despite being spoken by millions, lacks adequate computational resources, limiting its inclusion in digital and AI-driven applications. To address this gap, we introduce maiBERT, a BERT-based language model pre-trained specifically for Maithili using the Masked Language Modeling (MLM) technique. Our model is trained on a newly constructed Maithili corpus and evaluated through a news classification task. In our experiments, maiBERT achieved an accuracy of 87.02{\%}, outperforming existing regional models like NepBERTa and HindiBERT, with a 0.13{\%} overall accuracy gain and 5{--}7{\%} improvement across various classes. We have open-sourced maiBERT on Hugging Face, enabling further fine-tuning for downstream tasks such as sentiment analysis and Named Entity Recognition (NER)."
}