Multi-task Learning for Chinese Word Usage Errors Detection
This addresses the problem of improving writing skills for non-native Chinese learners, but it is incremental as it builds on existing multi-task learning methods.
The paper tackled the problem of automatically detecting Chinese word usage errors in non-native learners' writing by proposing a multi-task learning approach that leverages auxiliary tasks like POS-tagging and word log frequency prediction. It achieved state-of-the-art results on the HSK corpus data without using extra data.
Chinese word usage errors often occur in non-native Chinese learners' writing. It is very helpful for non-native Chinese learners to detect them automatically when learning writing. In this paper, we propose a novel approach, which takes advantages of different auxiliary tasks, such as POS-tagging prediction and word log frequency prediction, to help the task of Chinese word usage error detection. With the help of these auxiliary tasks, we achieve the state-of-the-art results on the performances on the HSK corpus data, without any other extra data.