An Error-Guided Correction Model for Chinese Spelling Error Correction
- URL: http://arxiv.org/abs/2301.06323v2
- Date: Mon, 20 Mar 2023 08:37:45 GMT
- Title: An Error-Guided Correction Model for Chinese Spelling Error Correction
- Authors: Rui Sun, Xiuyu Wu, Yunfang Wu
- Abstract summary: We propose an error-guided correction model (EGCM) to improve Chinese spelling correction.
Our model achieves superior performance against state-of-the-art approaches by a remarkable margin.
- Score: 13.56600372085612
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although existing neural network approaches have achieved great success on
Chinese spelling correction, there is still room to improve. The model is
required to avoid over-correction and to distinguish a correct token from its
phonological and visually similar ones. In this paper, we propose an
error-guided correction model (EGCM) to improve Chinese spelling correction. By
borrowing the powerful ability of BERT, we propose a novel zero-shot error
detection method to do a preliminary detection, which guides our model to
attend more on the probably wrong tokens in encoding and to avoid modifying the
correct tokens in generating. Furthermore, we introduce a new loss function to
integrate the error confusion set, which enables our model to distinguish
easily misused tokens. Moreover, our model supports highly parallel decoding to
meet real application requirements. Experiments are conducted on widely used
benchmarks. Our model achieves superior performance against state-of-the-art
approaches by a remarkable margin, on both the correction quality and
computation speed.
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