Cross-lingual Transfer Learning for Grammatical Error Correction
Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, Mamoru Komachi · 2020
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.