Abstract
The task of Chinese Grammatical Error Diagnosis (CGED) is considered challenging due to the diversity of error types and subtypes, as well as the imbalanced distribution of subtype occurrences and the emergence of new subtypes, which pose a threat to the generalization ability of CGED models. In this paper, we propose a sentence editing and character filling-based CGED strategy that conducts task decomposition and transformation based on different types of grammatical errors, and provides corresponding solutions. To improve error detection accuracy, a refined set of error types is designed to better utilize training data. The correction task is transformed into a character slot filling task, the performance of which, as well as its generalization for long-tail scenarios and the open domain, can be improved by large-scale pre-trained models. Experiments conducted on CGED evaluation datasets show that our approach outperforms comparison models in all evaluation metrics and has good generalization.
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Xie, H., Chen, X., Lyu, X., Liu, L. (2023). A Generalized Strategy of Chinese Grammatical Error Diagnosis Based on Task Decomposition and Transformation. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_11
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DOI: https://doi.org/10.1007/978-981-99-7224-1_11
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