TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification?

Taisei Enomoto, Hwichan Kim, Tosho Hirasawa, Yoshinari Nagai, Ayako Sato, Kyotaro Nakajima, Mamoru Komachi

Abstract
Lexical simplification (LS) is a process of replacing complex words with simpler alternatives to help readers understand sentences seamlessly. This process is divided into two primary subtasks: assessing word complexities and replacing high-complexity words with simpler alternatives. Employing task-specific supervised data to train models is a prevalent strategy for addressing these subtasks. However, such approach cannot be employed for low-resource languages. Therefore, this paper introduces a multilingual LS pipeline system that does not rely on supervised data. Specifically, we have developed systems based on GPT-4 for each subtask. Our systems demonstrated top-class performance on both tasks in many languages. The results indicate that GPT-4 can effectively assess lexical complexity and simplify complex words in a multilingual context with high quality.
Anthology ID:
2024.bea-1.52
Volume:
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
590–598
Language:
URL:
https://aclanthology.org/2024.bea-1.52
DOI:
Bibkey:
Cite (ACL):
Taisei Enomoto, Hwichan Kim, Tosho Hirasawa, Yoshinari Nagai, Ayako Sato, Kyotaro Nakajima, and Mamoru Komachi. 2024. TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification?. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 590–598, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
TMU-HIT at MLSP 2024: How Well Can GPT-4 Tackle Multilingual Lexical Simplification? (Enomoto et al., BEA 2024)
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