Entity-focused Chinese Spelling Correction: Dataset and Approach

Due to the strong representation capability of pre-trained language models, Chinese spelling correction models have significantly improved. However, pre-trained language models focus on contextualized information and treat all words equally, thus ignoring the entity information. In practical application, entity words are the most difficult part to handle in various artificial intelligence tasks, i.e., machine translation, optical character recognition, and automatic speech recognition. To address this issue, we first construct an entity-focused Chinese spelling correction dataset (EFCSC), the first public spelling correction corpus to emphasize entity errors. Furthermore, we propose an entity knowledge injected language model (EKILM) designed for entity-focused spelling correction, which injects entity information into pre-trained language models, thus ensuring traditional spelling correction models pay more attention to entity words. Experiments on several benchmark datasets show that our proposed model outperforms all strong baseline models, leading to state-of-the-art results on all datasets. Extensive experiments and detailed analyses demonstrate that our proposed model enhances entity error correction ability without damaging the normal spelling correction performance. Our code and dataset will be released at https://github.com/DPloved/EFCSC to facilitate future research.