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Semantic Refinement GRU-Based Neural Language Generation for Spoken Dialogue Systems

  • Van-Khanh Tran
  • Le-Minh Nguyen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 781)

Abstract

Natural language generation (NLG) plays a critical role in spoken dialogue systems. This paper presents a new approach to NLG by using recurrent neural networks (RNN), in which a gating mechanism is applied before RNN computation. This allows the proposed model to generate appropriate sentences. The RNN-based generator can be learned from unaligned data by jointly training sentence planning and surface realization to produce natural language responses. The model was extensively evaluated on four different NLG domains. The results show that the proposed generator achieved better performance on all the NLG domains compared to previous generators.

Notes

Acknowledgment

This work was supported by the JSPS KAKENHI Grant number JP15K16048.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Japan Advanced Institute of Science and Technology, JAISTNomiJapan
  2. 2.University of Information and Communication Technology, ICTU, Thai Nguyen UniversityThai NguyenVietnam

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