@inproceedings{mun-shin-2025-polysemy,
    title = "Polysemy Interpretation and Transformer Language Models: A Case of {K}orean Adverbial Postposition -(u)lo",
    author = "Mun, Seongmin  and
      Shin, Gyu-Ho",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.105/",
    pages = "1555--1561",
    abstract = "This study examines how Transformer language models utilise lexico-phrasal information to interpret the polysemy of the Korean adverbial postposition -(u)lo. We analysed the attention weights of both a Korean pre-trained BERT model and a fine-tuned version. Results show a general reduction in attention weights following fine-tuning, alongside changes in the lexico-phrasal information used, depending on the specific function of -(u)lo. These findings suggest that, while fine-tuning broadly affects a model{'}s syntactic sensitivity, it may also alter its capacity to leverage lexico-phrasal features according to the function of the target word."
}