WAIS: Word Attention for Joint Intent Detection and Slot Filling

Authors

  • Sixuan Chen Hong Kong University of Science and Technology
  • Shuai Yu Fudan University

DOI:

https://doi.org/10.1609/aaai.v33i01.33019927

Abstract

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved a state-of-the-art performance. Most previous works exploited semantic level information to calculate the attention weights. However, few works have taken the importance of word level information into consideration. In this paper, we propose WAIS, word attention for joint intent detection and slot filling. Considering that intent detection and slot filling have a strong relationship, we further propose a fusion gate that integrates the word level information and semantic level information together for jointly training the two tasks. Extensive experiments show that the proposed model has robust superiority over its competitors and sets the state-of-the-art.

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Published

2019-07-17

How to Cite

Chen, S., & Yu, S. (2019). WAIS: Word Attention for Joint Intent Detection and Slot Filling. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9927-9928. https://doi.org/10.1609/aaai.v33i01.33019927

Issue

Section

Student Abstract Track