Using phonetic and visual knowledge to improve Chinese spelling correction models

Xin Wang; Xiaoguang Mao

doi:10.1117/12.2640875

2 December 2022 Using phonetic and visual knowledge to improve Chinese spelling correction models

Xin Wang, Xiaoguang Mao

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122880H (2022) https://doi.org/10.1117/12.2640875
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

Since the birth of BERT, Bert class models have achieved good results in Chinese Spelling Correction (CSC) tasks. However, the existing CSC models do not thoroughly learn typos' phonetic and visual knowledge. We propose a pretraining method and an Error-Prone Sentence Generation (EPSC) algorithm for training to overcome the shortcomings of existing CSC models. Experiments show that our method can improve the performance of BERT and soft-masked BERT in CSC tasks.

Citation Download Citation

Xin Wang and Xiaoguang Mao "Using phonetic and visual knowledge to improve Chinese spelling correction models", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122880H (2 December 2022); https://doi.org/10.1117/12.2640875

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