SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check

@article{Cheng2020SpellGCNIP,
  title={SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check},
  author={Xingyi Cheng and Weidi Xu and Kun-Long Chen and Shaohua Jiang and Feng Wang and Taifeng Wang and Wei Chu and Yuan Qi},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.14166}
}
  • Xingyi Cheng, Weidi Xu, +5 authors Yuan Qi
  • Published 2020
  • Computer Science
  • ArXiv
  • Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN). The model builds… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 32 REFERENCES