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.
Anthology ID:
2020.coling-main.415
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4704–4715
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.415
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.415.pdf