Improving Chinese Spelling Correction with Text-Phonetics Differentiation and Adaptive Fusion

Abstract

Chinese Spelling Correction (CSC) aims to detect and correct the misspelled characters in Chinese texts. Recent studies have achieved great success by incorporating the phonetic information for task predictions. Still, existing methods suffer from two limitations: 1) The differentiated information between textual characters and Pinyin pronunciation are underexplored. 2) The task predictions are performed with the over-emphasises on either the textual or phonetic sequence, ignoring the balanced modeling and adaptive fusion. In this work, we proposed a method to alleviate the issues above. For the first issue, a Coupled Attention Module (CAM) is proposed where a couple of attention functions capture the associated and differentiated text-phonetics information simultaneously. For the second issue, an adaptive fusion is designed to derive the phonetics-aware textual representations and text-aware phonetic representations for task predictions. Experiments on three public benchmarks demonstrate the effectiveness of our proposed method.