@inproceedings{ide-etal-2023-japanese,
title = "{J}apanese Lexical Complexity for Non-Native Readers: A New Dataset",
author = "Ide, Yusuke and
Mita, Masato and
Nohejl, Adam and
Ouchi, Hiroki and
Watanabe, Taro",
editor = {Kochmar, Ekaterina and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Madnani, Nitin and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng and
Zesch, Torsten},
booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bea-1.40",
doi = "10.18653/v1/2023.bea-1.40",
pages = "477--487",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Japanese Lexical Complexity for Non-Native Readers: A New Dataset
%A Ide, Yusuke
%A Mita, Masato
%A Nohejl, Adam
%A Ouchi, Hiroki
%A Watanabe, Taro
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ide-etal-2023-japanese
%X 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.
%R 10.18653/v1/2023.bea-1.40
%U https://aclanthology.org/2023.bea-1.40
%U https://doi.org/10.18653/v1/2023.bea-1.40
%P 477-487
Markdown (Informal)
[Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://aclanthology.org/2023.bea-1.40) (Ide et al., BEA 2023)
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.