Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 136-141.doi: 10.11896/jsjkx.210100025

• Intelligent Computing • Previous Articles     Next Articles

End-to-End Chinese-Braille Automatic Conversion Based on Transformer

JIANG Qi1, SU Wei1, XIE Ying2, ZHOUHONG An-ping2, ZHANG Jiu-wen1, CAI Chuan1   

  1. 1 School of Information Science & Engineering,Lanzhou University,Lanzhou 730000,China
    2 China Braille Press,Beijing 100142,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:JIANG Qi,born in 1995,postgraduate.His main research interests include natural language processing and Chinese-Braille conversion technology.
    SU Wei,born in 1977,associate professor.His main research interests include natural language processing and information accessibility technology.
  • Supported by:
    National Natural Science Foundation of China(61772006),Research Program of China Disabled Persons' Federation and China Association of the Blind((14)0218),Guangxi Province Science and Technology Project(AA17204096,AB17129012) and Fund for Guangxi Province “Baguischolars”.

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Abstract: Chinese-Braille automatic conversion concerns the life and learning of 17 million visually impaired people in China and the national information accessibility construction.All existing Chinese-Braille conversion methods adopt multi-step process,which firstly segment Chinese text according to Braille word segmentation rules,then mark tone for Chinese characters.This paper studies end-to-end deep learning system that directly converts Chinese into Braille.The encoder-decoder model transformer is trained on Chinese-Braille corpus.Based on six-month data of People's Daily,totaling about 12 million characters,this paper builds three Chinese-Braille corpora of Chinese common Braille,current Braille and Chinese double-phonic Braille systems.The experimental results show that the method proposed in this paper can convert Chinese into Braille in one step,and reaches BLEU score of 80.25%,79.08% and 79.29% in Chinese common Braille,current Braille and Chinese double-phonic Braille.Compared with the existing methods,this method requires a corpus which is less difficult to construct and the engineering complexity is lower.

Key words: Chinese-braille conversion, Encoder-decoder model, End-to-end deep learning, Transformer

CLC Number: 

  • TP391
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