The primary objective of Chinese spelling correction(CSC)is to detect and correct erroneous characters in Chinese text,which can result from various factors,such as inaccuracies in pinyin representation,character rese...The primary objective of Chinese spelling correction(CSC)is to detect and correct erroneous characters in Chinese text,which can result from various factors,such as inaccuracies in pinyin representation,character resemblance,and semantic discrepancies.However,existing methods often struggle to fully address these types of errors,impacting the overall correction accuracy.This paper introduces a multi-modal feature encoder designed to efficiently extract features from three distinct modalities:pinyin,semantics,and character morphology.Unlike previous methods that rely on direct fusion or fixed-weight summation to integrate multi-modal information,our approach employs a multi-head attention mechanism to focuse more on relevant modal information while dis-regarding less pertinent data.To prevent issues such as gradient explosion or vanishing,the model incorporates a residual connection of the original text vector for fine-tuning.This approach ensures robust model performance by maintaining essential linguistic details throughout the correction process.Experimental evaluations on the SIGHAN benchmark dataset demonstrate that the pro-posed model outperforms baseline approaches across various metrics and datasets,confirming its effectiveness and feasibility.展开更多
基金Supported by the National Natural Science Foundation of China(No.61472256,61170277)the Hujiang Foundation(No.A14006).
文摘The primary objective of Chinese spelling correction(CSC)is to detect and correct erroneous characters in Chinese text,which can result from various factors,such as inaccuracies in pinyin representation,character resemblance,and semantic discrepancies.However,existing methods often struggle to fully address these types of errors,impacting the overall correction accuracy.This paper introduces a multi-modal feature encoder designed to efficiently extract features from three distinct modalities:pinyin,semantics,and character morphology.Unlike previous methods that rely on direct fusion or fixed-weight summation to integrate multi-modal information,our approach employs a multi-head attention mechanism to focuse more on relevant modal information while dis-regarding less pertinent data.To prevent issues such as gradient explosion or vanishing,the model incorporates a residual connection of the original text vector for fine-tuning.This approach ensures robust model performance by maintaining essential linguistic details throughout the correction process.Experimental evaluations on the SIGHAN benchmark dataset demonstrate that the pro-posed model outperforms baseline approaches across various metrics and datasets,confirming its effectiveness and feasibility.
