Cross-lingual Transfer Learning for Grammatical Error Correction
Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, Mamoru Komachi
Abstract
In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident. Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.
BibTeX
@inproceedings{yamashita-etal-2020-cross,
title = "Cross-lingual Transfer Learning for Grammatical Error Correction",
author = "Yamashita, Ikumi and
Katsumata, Satoru and
Kaneko, Masahiro and
Imankulova, Aizhan and
Komachi, Mamoru",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.415/",
doi = "10.18653/v1/2020.coling-main.415",
pages = "4704--4715"
}