Multi-task Learning for Chinese Word Usage Errors Detection

TL;DR

This paper introduces a multi-task learning approach leveraging auxiliary tasks like POS-tagging and word frequency prediction to improve Chinese word usage error detection, achieving state-of-the-art results on the HSK corpus without additional data.

Contribution

It presents a novel multi-task learning framework that enhances Chinese word usage error detection by incorporating auxiliary linguistic tasks.

Findings

Achieved state-of-the-art performance on HSK corpus data.

Utilized auxiliary tasks to improve error detection accuracy.

No extra data required for the proposed method.

Abstract

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.