COLING 2025main0 citations
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.
BibTeX
@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://aclanthology.org/2025.coling-main.105/",
pages = "1555--1561"
}