@inproceedings{vidal-gorene-etal-2024-cross,
    title = "Cross-Dialectal Transfer and Zero-Shot Learning for {A}rmenian Varieties: A Comparative Analysis of {RNN}s, Transformers and {LLM}s",
    author = "Vidal-Gor{\`e}ne, Chahan  and
      Tomeh, Nadi  and
      Khurshudyan, Victoria",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Miyagawa, So  and
      Alnajjar, Khalid  and
      Bizzoni, Yuri},
    booktitle = "Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities",
    month = nov,
    year = "2024",
    address = "Miami, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.nlp4dh-1.42/",
    doi = "10.18653/v1/2024.nlp4dh-1.42",
    pages = "438--449",
    abstract = "This paper evaluates lemmatization, POS-tagging, and morphological analysis for four Armenian varieties: Classical Armenian, Modern Eastern Armenian, Modern Western Armenian, and the under-documented Getashen dialect. It compares traditional RNN models, multilingual models like mDeBERTa, and large language models (ChatGPT) using supervised, transfer learning, and zero/few-shot learning approaches. The study finds that RNN models are particularly strong in POS-tagging, while large language models demonstrate high adaptability, especially in handling previously unseen dialect variations. The research highlights the value of cross-variational and in-context learning for enhancing NLP performance in low-resource languages, offering crucial insights into model transferability and supporting the preservation of endangered dialects."
}