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An Interactive Two-Pass Decoding Network for Joint Intent Detection and Slot Filling

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Natural Language Processing and Chinese Computing (NLPCC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12431))

Abstract

Intent detection and slot filling are two closely related tasks for building a spoken language understanding (SLU) system. The joint methods for the two tasks focus on modeling the semantic correlations between the intent and slots and applying the information of one task to guide the other task, which helps them to promote each other. However, most existing joint approaches only unidirectionally utilize the intent information to guide slot filling while ignoring the fact that the slot information is beneficial to intent detection. To address this issue, in this paper, we propose an Interactive Two-pass Decoding Network (ITD-Net) for joint intent detection and slot filling, which explicitly establishes the token-level interactions between the intent and slots through performing an interactive two-pass decoding process. In ITD-Net, the task-specific information obtained by the first-pass decoder for one task is directly fed into the second-pass decoder for the other task, which can take full advantage of the explicit intent and slot information to achieve bidirectional guidance between the two tasks. Experiments on the ATIS and SNIPS datasets demonstrate the effectiveness and superiority of our ITD-Net.

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Notes

  1. 1.

    https://github.com/snipsco/nlu-benchmark/tree/master/2017-06-custom-intent-engines.

  2. 2.

    For the SF-ID Network, we adopt the results of the model using the ID-First mode from [5]. For all other baselines, we obtain the results from [14].

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Acknowledgments

This paper is supported by National Key Research and Development Program of China under Grant No.2017YFB0803003 and National Science Foundation for Young Scientists of China (Grant No.61702507).

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Huailiang Peng, Mengjun Shen, Lei Jiang, Qiong Dai & Jianlong Tan

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Huailiang Peng, Mengjun Shen, Lei Jiang, Qiong Dai & Jianlong Tan

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Huailiang Peng

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  1. Huailiang Peng
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  2. Mengjun Shen
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  3. Lei Jiang
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  4. Qiong Dai
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Corresponding author

Correspondence to Lei Jiang .

Editor information

Editors and Affiliations

  1. ECE & Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada

    Xiaodan Zhu

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Min Zhang

  3. School of Computer Science and Technology, Soochow University, Suzhou, China

    Yu Hong

  4. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Ruifang He

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Peng, H., Shen, M., Jiang, L., Dai, Q., Tan, J. (2020). An Interactive Two-Pass Decoding Network for Joint Intent Detection and Slot Filling. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-60457-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60456-1

  • Online ISBN: 978-3-030-60457-8

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