@inproceedings{39033a35132c4e3eba4bdc92dea1b1ff,
title = "Polysemy Interpretation and Transformer Language Models: A Case of Korean Adverbial Postposition -(u)lo",
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.",
author = "Seongmin Mun and Shin, \{Gyu Ho\}",
note = "Publisher Copyright: {\textcopyright} 2025 Association for Computational Linguistics.; 31st International Conference on Computational Linguistics, COLING 2025 ; Conference date: 19-01-2025 Through 24-01-2025",
year = "2025",
language = "English",
series = "Proceedings - International Conference on Computational Linguistics, COLING",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1555--1561",
editor = "Owen Rambow and Leo Wanner and Marianna Apidianaki and Hend Al-Khalifa and \{Di Eugenio\}, Barbara and Steven Schockaert",
booktitle = "Main Conference",
address = "United States",
}