@inproceedings{lee-etal-2024-manner,
    title = "{M}an{NER} {\&} {M}an{POS}: Pioneering {NLP} for Endangered {M}anchu Language",
    author = "Lee, Sangah  and
      Byun, Sungjoo  and
      Seo, Jean  and
      Kang, Minha",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.961",
    pages = "11030--11039",
    abstract = "We present pioneering research in the realm of Natural Language Processing (NLP) for the endangered Manchu language. Recognizing the critical importance of linguistic preservation, we experiment with three language models {--} BiLSTM-CRF, BERT, and mBERT {--} for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks. Given the limited digitized Manchu text available, we augment the data using GloVe embeddings for the pre-training of BERT-based models. Remarkably, all models demonstrated outstanding performance, achieving over 90{\%} F1 score in both NER and POS tagging tasks. Our research not only marks the first application of NLP on Manchu and the inaugural use of BERT-based models for the language but also stands as the first endeavor to employ Manchu for NER and POS tagging. To foster further exploration and applications in the field, we make our fine-tuning dataset and models available to the public. Through this research, we aim to underscore the significance of NLP in the protection and revitalization of low-resource languages.",
}

We present pioneering research in the realm of Natural Language Processing (NLP) for the endangered Manchu language. Recognizing the critical importance of linguistic preservation, we experiment with three language models {--} BiLSTM-CRF, BERT, and mBERT {--} for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks. Given the limited digitized Manchu text available, we augment the data using GloVe embeddings for the pre-training of BERT-based models. Remarkably, all models demonstrated outstanding performance, achieving over 90{\%} F1 score in both NER and POS tagging tasks. Our research not only marks the first application of NLP on Manchu and the inaugural use of BERT-based models for the language but also stands as the first endeavor to employ Manchu for NER and POS tagging. To foster further exploration and applications in the field, we make our fine-tuning dataset and models available to the public. Through this research, we aim to underscore the significance of NLP in the protection and revitalization of low-resource languages.
Lee, Sangah Byun, Sungjoo Seo, Jean Kang, Minha
ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language