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Big Data Mining and Analytics 2022, 5(1): 41-52 https://doi.org/10.26599/BDMA.2021.9020013
Open Access | Issue | Published: 27 December 2021

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

Show Author's Information Hui Bai1,  Yan Yang1( ),  Jie Wang1
School of Computing and Artifical Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Keywords:
slot-relevant attention, multi-domain dialog state tracking, task-oriented dialog system
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information Hui Bai1, Yan Yang1( ), Jie Wang1
School of Computing and Artifical Intelligence, Southwest Jiaotong University, Chengdu 611756, China

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 attention, multi-domain dialog state tracking, task-oriented dialog system

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Publication history

Received: 07 July 2021
Accepted: 19 July 2021
Published: 27 December 2021
Issue date: March 2022

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© The author(s) 2022.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61976247).

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