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Open Access

Exploiting More Associations Between Slots for Multi-Domain Dialog State Tracking

School of Computing and Artifical Intelligence, Southwest Jiaotong University, Chengdu 611756, China
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Abstract

Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the correlation between slots, so that the slots can incorporate more slot-value transformer information. Experimental results show that our approach has performed well on the Multi-domain Wizard-Of-Oz (MultiWOZ) 2.0 and MultiWOZ2.1 datasets, demonstrating the effectiveness and necessity of incorporating slot-relevant information.

Keywords

slot-relevant attentionmulti-domain dialog state trackingtask-oriented dialog system

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Big Data Mining and Analytics
Volume 5 Issue 1,
March 2022
Pages 41-52
DOI: 10.26599/BDMA.2021.9020013
Cite this article:
Bai H, Yang Y, Wang J. Exploiting More Associations Between Slots for Multi-Domain Dialog State Tracking. Big Data Mining and Analytics, 2022, 5(1): 41-52. https://doi.org/10.26599/BDMA.2021.9020013

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Received: 07 July 2021
Accepted: 19 July 2021
Published: 27 December 2021
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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