Chinese spelling correction is essential in text editing.Most of the existing Chinese spelling error correction models are single input models,and there are limitations in the semantic information and error correction results of the models.In this paper,a multi-input fusion spelling error correction method based on contrast optimization,MIF-SECCO,is proposed.MIF-SECCO contains two stages:multi-input semantic learning and contrast learning-driven semantic fusion error correction.In the first stage,preliminary error correction results from multiple single input models are integrated to provide sufficient complementary semantic information for semantic fusion.In the second stage,multiple complementary sentence semantics are optimized based on the contrastive learning approach to avoid over-correction of sentences by the model.The limitations of error correction results of the model are improved by fusing multiple complementary semantics for re-correction of erroneous sentences.Experimental results on the public datasets SIGHAN13,SIGHAN14 and SIGHAN15 demonstrate MIF-SECCO effectively improves the error correction performance of the model.
关键词
中文拼写纠错/多输入语义学习/互补语义融合/对比学习优化
Key words
Chinese Spelling Error Correction/Multi-input Semantic Learning/Complementary Seman-tic Fusion/Contrastive Learning Optimization
维普
万方数据