An Alignment-Agnostic Model for Chinese Text Error Correction

TL;DR

This paper introduces an alignment-agnostic Chinese text error correction model that effectively handles mistaken, missing, and redundant characters, outperforming existing methods especially in non-aligned scenarios.

Contribution

The paper proposes a novel alignment-agnostic detect-correct framework for Chinese text correction, capable of handling various error types and functioning without annotated data.

Findings

Achieves superior performance on three datasets

Handles both aligned and non-aligned text correction scenarios

Effective as a cold start model without annotated data

Abstract

This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which is common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters errors, but they cannot deal with missing or redundant characters. The reason is that lengths of sentences before and after correction are not the same, leading to the inconsistence between model inputs and outputs. Although the Seq2Seq-based or sequence tagging methods provide solutions to the problem and achieved relatively good results on English context, but they do not perform well in Chinese context according to our experimental results. In our work, we propose a novel detect-correct framework which is alignment-agnostic, meaning that it can handle both text aligned and non-aligned occasions, and it can also serve as a cold start model when there are no annotated data provided. Experimental results on three datasets demonstrate that our method is effective and achieves the best performance among existing published models.