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)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4704–4715
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.415
- DOI:
- 10.18653/v1/2020.coling-main.415
- Cite (ACL):
- Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, and Mamoru Komachi. 2020. Cross-lingual Transfer Learning for Grammatical Error Correction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4704–4715, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Cross-lingual Transfer Learning for Grammatical Error Correction (Yamashita et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.415.pdf
- Data
- AKCES-GEC