An Effective Non-Autoregressive Model for Spoken Language Understanding

  title={An Effective Non-Autoregressive Model for Spoken Language Understanding},
  author={Lizhi Cheng and Weijia Jia and Wenmian Yang},
  journal={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  • Lizhi Cheng, Weijia Jia, Wenmian Yang
  • Published 16 August 2021
  • Computer Science
  • Proceedings of the 30th ACM International Conference on Information & Knowledge Management
Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference latency due to the impatience of humans. Non-autoregressive SLU models clearly increase the inference speed but suffer uncoordinated-slot problems caused by the lack of sequential dependency information among each slot chunk. To gap this shortcoming, in this paper, we propose a novel non-autoregressive SLU model named Layered-Refine Transformer, which contains a Slot Label… 

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