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JACIII Vol.27 No.3 pp. 511-521
doi: 10.20965/jaciii.2023.p0511
(2023)

Research Paper:

Effectiveness of Pre-Trained Language Models for the Japanese Winograd Schema Challenge

Keigo Takahashi ORCID Icon, Teruaki Oka, and Mamoru Komachi ORCID Icon

Graduate School of System Design, Tokyo Metropolitan University (TMU)
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Received:
September 13, 2022
Accepted:
February 13, 2023
Published:
May 20, 2023
Keywords:
natural language processing, Winograd schema challenge, reference resolution, Japanese
Abstract

This paper compares Japanese and multilingual language models (LMs) in a Japanese pronoun reference resolution task to determine the factors of LMs that contribute to Japanese pronoun resolution. Specifically, we tackle the Japanese Winograd schema challenge task (WSC task), which is a well-known pronoun reference resolution task. The Japanese WSC task requires inter-sentential analysis, which is more challenging to solve than intra-sentential analysis. A previous study evaluated pre-trained multilingual LMs in terms of training language on the target WSC task, including Japanese. However, the study did not perform pre-trained LM-wise evaluations, focusing on the training language-wise evaluations with a multilingual WSC task. Furthermore, it did not investigate the effectiveness of factors (e.g., model size, learning settings in the pre-training phase, or multilingualism) to improve the performance. In our study, we compare the performance of inter-sentential analysis on the Japanese WSC task for several pre-trained LMs, including multilingual ones. Our results confirm that XLM, a pre-trained LM on multiple languages, performs the best among all considered LMs, which we attribute to the amount of data in the pre-training phase.

Transition before (left) and after (right) fine-tuning XLM

Transition before (left) and after (right) fine-tuning XLM

Cite this article as:
K. Takahashi, T. Oka, and M. Komachi, “Effectiveness of Pre-Trained Language Models for the Japanese Winograd Schema Challenge,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 511-521, 2023.
Data files:
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