@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",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Polysemy Interpretation and Transformer Language Models: A Case of Korean Adverbial Postposition -(u)lo
%A Mun, Seongmin
%A Shin, Gyu-Ho
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F mun-shin-2025-polysemy
%X 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.
%U https://aclanthology.org/2025.coling-main.105/
%P 1555-1561
Markdown (Informal)
[Polysemy Interpretation and Transformer Language Models: A Case of Korean Adverbial Postposition -(u)lo](https://aclanthology.org/2025.coling-main.105/) (Mun & Shin, COLING 2025)
ACL