Abstract
Automatic Chinese spelling checking and correction (CSC) is currently a challenging task especially when the sentence is complex in semantics and expressions. Meanwhile, a CSC model normally requires a huge amount of training corpus which is usually unavailable. To capture the semantic information of sentences, this paper proposes an approach (named as DPL-Corr) based on character-based pre-trained contextual representations, which helps to significantly improve the performance of CSC. In DPL-Corr, the module of spelling checking is a sequence-labeling model enhanced by deep contextual semantics analysis, and the module of spelling correction is a masked language model integrated with multilayer filtering to obtain the final corrections. Based on experiments on SIGHAN 2015 dataset, DPL-Corr achieves a significantly better performance of CSC than conventional models.
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Acknowledgement
This work is supported by the projects of National Natural Science Foundation of China (No. 61472014, No. 61573028 and No. 61432020), the Natural Science Foundation of Beijing (No. 4142023) and the Beijing Nova Program (XX2015B010). We also thank all the anonymous reviewers for their valuable comments.
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Xie, H. et al. (2019). Automatic Chinese Spelling Checking and Correction Based on Character-Based Pre-trained Contextual Representations. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_49
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DOI: https://doi.org/10.1007/978-3-030-32236-6_49
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