# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1904.01783