Research on Domain-Specific Chinese Spelling Correction Method Based on Plugin Extension Modules
This addresses the limitation of existing models for users handling specialized Chinese texts, though it is incremental as it builds on general-domain models with domain-specific enhancements.
The paper tackled the problem of poor Chinese spelling correction performance on domain-specific texts by proposing a plugin extension module that learns domain terminology features, resulting in significantly improved correction accuracy in medical, legal, and official document domains compared to a baseline model.
This paper proposes a Chinese spelling correction method based on plugin extension modules, aimed at addressing the limitations of existing models in handling domain-specific texts. Traditional Chinese spelling correction models are typically trained on general-domain datasets, resulting in poor performance when encountering specialized terminology in domain-specific texts. To address this issue, we design an extension module that learns the features of domain-specific terminology, thereby enhancing the model's correction capabilities within specific domains. This extension module can provide domain knowledge to the model without compromising its general spelling correction performance, thus improving its accuracy in specialized fields. Experimental results demonstrate that after integrating extension modules for medical, legal, and official document domains, the model's correction performance is significantly improved compared to the baseline model without any extension modules.