Multi-input Fusion Spelling Error Correction Model Based on Contrast Optimization
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.
Chinese Spelling Error CorrectionMulti-input Semantic LearningComplementary Seman-tic FusionContrastive Learning Optimization
万方数据
维普
