Japanese Lexical Complexity for Non-Native Readers: A New Dataset

Yusuke Ide, Masato Mita, Adam Nohejl, Hiroki Ouchi, Taro Watanabe


Abstract
Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex words to assist readers. To study lexical complexity in Japanese, we construct the first Japanese LCP dataset. Our dataset provides separate complexity scores for Chinese/Korean annotators and others to address the readers’ L1-specific needs. In the baseline experiment, we demonstrate the effectiveness of a BERT-based system for Japanese LCP.
Anthology ID:
2023.bea-1.40
Volume:
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
477–487
Language:
URL:
https://aclanthology.org/2023.bea-1.40
DOI:
10.18653/v1/2023.bea-1.40
Bibkey:
Cite (ACL):
Yusuke Ide, Masato Mita, Adam Nohejl, Hiroki Ouchi, and Taro Watanabe. 2023. Japanese Lexical Complexity for Non-Native Readers: A New Dataset. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 477–487, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Japanese Lexical Complexity for Non-Native Readers: A New Dataset (Ide et al., BEA 2023)
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