Grammatical Error Correction with Pre-trained Model and Multilingual Learner Corpus for Cross-lingual Transfer Learning
Abstrakt
In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Few studies have investigated the use of knowledge from other languages for GEC; therefore, it is unclear if useful grammatical knowledge can be transferred.
There are often common grammatical items between similar languages, and it may be possible to perform cross-lingual transfer learning by exploiting their grammatical similarities. In this study, we use pre-trained model and multilingual learner corpus for cross-lingual transfer learning 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 signi cant impact on the accuracy of correcting certain types of errors.