Nawei Zhong
ABSTRACT
Chinese grammar error correction (CGEC) is a basic application of natural language processing. It can will detect and correct various grammatical errors in the texts. In this paper, we propose a Hybrid Chinese Grammar error checking Model based on Transformer. First, the model introduces Bert pre-training model to process text sequences at the Chinese character level. Next, we integrate point mutual information (PMI) to enable the model to deal with the semantic collocation information between Chinese characters. We benchmark a baseline model against our model on CGED_2018 dataset and Corrupt dataset. The proposed method is better than the baseline model, and obtains state-of-the-art results on CGED_2018 datasets.
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Index Terms
- Hybrid Chinese Grammar Error Checking Model Based on Transformer
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