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A Heterogeneous Interaction Graph Network for Multi-Intent Spoken Language Understanding

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Abstract

As the core component of intelligent dialogue systems, spoken language understanding (SLU) usually includes two tasks: intent detection and slot filling. In real-world scenarios, users may express multiple intents in an utterance, and a token-level slot label can belong to multiple intents. Intent detection and slot filling tasks are closely related and instruct each other. In this paper, we propose the heterogeneous interaction graph framework with window mechanism for joint multi-intent detection and slot filling, which can adequately capture the rich semantic information of different granularity in heterogeneous information. We leverage different types of nodes and edges to construct the heterogeneous graph to realize the interaction between coarse-grained sentence-level intent information and fine-grained word-level slot information. And we utilize window mechanism to accommodate the temporal locality of the slot information. Experimental results on two datasets show that our model achieves the state-of-the-art performance. Comprehensive analysis empirically verifies the effectiveness of each component.

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Data Availibility Statement

MixATIS and MixSNIPS datasets: https://github.com/LooperXX/AGIF.

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Authors and Affiliations

  1. College of Computer Science and technology, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, Heilongjiang, China

    Qichen Zhang, Shuai Wang & Jingmei Li

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  1. Qichen Zhang
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  2. Shuai Wang
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  3. Jingmei Li
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Correspondence to Qichen Zhang.

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No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

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Zhang, Q., Wang, S. & Li, J. A Heterogeneous Interaction Graph Network for Multi-Intent Spoken Language Understanding. Neural Process Lett 55, 9483–9501 (2023). https://doi.org/10.1007/s11063-023-11210-7

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