DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check

TL;DR

This paper introduces DISC, a lightweight, plug-and-play module that enhances Chinese spelling checkers by leveraging phonetic and glyph similarities during inference, improving accuracy without retraining.

Contribution

The paper presents a novel similarity-based decoding intervention module that can be integrated into existing CSC models without additional training.

Findings

Significant performance improvements on three benchmarks.

Approaching or surpassing state-of-the-art models.

Easy integration with various CSC models.

Abstract

One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph. To accommodate this, previous works usually leverage confusion sets, which suffer from two problems, i.e., difficulty in determining which character pairs to include and lack of probabilities to distinguish items in the set. In this paper, we propose a light-weight plug-and-play DISC (i.e., decoding intervention with similarity of characters) module for CSC models.DISC measures phonetic and glyph similarities between characters and incorporates this similarity information only during the inference phase. This method can be easily integrated into various existing CSC models, such as ReaLiSe, SCOPE, and ReLM, without additional training costs. Experiments on three CSC benchmarks demonstrate that our proposed method significantly improves model performance, approaching and even surpassing the current state-of-the-art models.