Streamlined decoder for Chinese spoken language understanding

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As a critical component of Spoken Dialog System (SDS), spoken language understanding (SLU) attracts a lot of attention, especially for methods based on unaligned data. Recently, a new approach has been proposed that utilizes the hierarchical relationship between act-slot-value triples. However, it ignores the transfer of internal information which may record the intermediate information of the upper level and contribute to the prediction of the lower level. So, we propose a novel streamlined decoding structure with attention mechanism, which uses three successively connected RNN to decode act, slot and value respectively. On the first Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU), our model exceeds state-of-the-art model on an unaligned multi-turn task-oriented Chinese spoken dialogue dataset provided by the contest.

Original languageEnglish
Title of host publicationICMI 2019 - Proceedings of the 2019 International Conference on Multimodal Interaction
EditorsWen Gao, Helen Mei Ling Meng, Matthew Turk, Susan R. Fussell, Bjorn Schuller, Bjorn Schuller, Yale Song, Kai Yu
PublisherAssociation for Computing Machinery, Inc
Pages516-520
Number of pages5
ISBN (Electronic)9781450368605
DOIs
Publication statusPublished - 14 Oct 2019
Event21st ACM International Conference on Multimodal Interaction, ICMI 2019 - Suzhou, China
Duration: 14 Oct 201918 Oct 2019

Publication series

NameICMI 2019 - Proceedings of the 2019 International Conference on Multimodal Interaction

Conference

Conference21st ACM International Conference on Multimodal Interaction, ICMI 2019
Country/TerritoryChina
CitySuzhou
Period14/10/1918/10/19

Keywords

  • Attention mechanisms
  • Long short term memory networks
  • Pointer network
  • Spoken dialog system
  • Spoken language understanding
  • Streamlined decoder

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