A deep bidirectional highway long short-term memory network approach to Chinese semantic role labeling

Qi Xia, Chung Hsing Yeh, Xiang Yu Chen

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Existing approaches to Chinese semantic role labeling (SRL) mainly adopt deep long short-term memory (LSTM) neural networks to address the long-term dependencies problem. However, deep LSTM networks cannot address the vanishing gradient problem properly. In addition, the complexity of the Chinese language, as a hieroglyphic language, decreases the performance of traditional SRL approaches to Chinese SRL. To address these problems, this paper proposes a new approach with a deep bidirectional highway LSTM network. The performance of the proposed approach is further improved by introducing the conditional random fields (CRFs) constraints and part-of-speech (POS) feature since POS tags are the classes of formal equivalents of words in linguistics. The experimental results on the commonly used Chinese Proposition Bank dataset show that the proposed approach outperforms existing approaches. With an easily acquired and reliable POS feature for practical applications, the proposed approach substantially improves Chinese SRL.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN) 2019
EditorsPlamen Angelov, Manuel Roveri
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728119854
ISBN (Print)9781728119861
Publication statusPublished - 2019
EventIEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019
https://ieeexplore.ieee.org/xpl/conhome/8840768/proceeding (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2019
Abbreviated titleIJCNN 2019
Internet address


  • Chinese language
  • conditional random field
  • deep bidirectional highway LSTM networks
  • part-of-speech
  • Semantic role labeling

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