SMGN: A State Memory Graph Network for Dialogue State Tracking

ZHANG Zhi-chang; YU Pei-lin; PANG Ya-li; ZHU Lin; ZENG Yang-yang

doi:10.12263/DZXB.20201463

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SMGN: A State Memory Graph Network for Dialogue State Tracking

PAPERS | 更新时间：2025-12-08

- SMGN: A State Memory Graph Network for Dialogue State Tracking
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 1851-1858(2022)
- 作者机构：
  
  西北师范大学计算机科学与工程学院，甘肃兰州 730070
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201463
  CLC： TP391.1;
- Received：18 December 2020，
  
  Revised：2021-04-15，
  
  Published：25 August 2022
- 稿件说明：
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张志昌,于沛霖,庞雅丽等.SMGN：用于对话状态跟踪的状态记忆图网络[J].电子学报,2022,50(08):1851-1858.

ZHANG Zhi-chang,YU Pei-lin,PANG Ya-li,et al.SMGN: A State Memory Graph Network for Dialogue State Tracking[J].ACTA ELECTRONICA SINICA,2022,50(08):1851-1858.
张志昌,于沛霖,庞雅丽等.SMGN：用于对话状态跟踪的状态记忆图网络[J].电子学报,2022,50(08):1851-1858. DOI： 10.12263/DZXB.20201463.

ZHANG Zhi-chang,YU Pei-lin,PANG Ya-li,et al.SMGN: A State Memory Graph Network for Dialogue State Tracking[J].ACTA ELECTRONICA SINICA,2022,50(08):1851-1858. DOI： 10.12263/DZXB.20201463.

摘要

对话状态跟踪是任务型对话系统的重要模块.已有研究使用注意力机制模拟图结构来引入历史信息，但这种方法无法显式利用对话状态的结构性.此外，如何生成复杂格式的对话状态也为研究带来了挑战.针对以上问题，本文提出一种状态记忆图网络SMGN（State Memory Graph Network）.该网络通过状态记忆图保存历史对话信息，并使用图结构与当前对话进行特征交互.本文还设计了一种基于状态记忆图的复杂对话状态生成方法.实验结果表明，本文提出的方法在CrossWOZ数据集上联合正确率提高1.39%，在MultiWOZ数据集上提高1.86%.

Abstract

Dialogue state tracking is an important module of task-oriented dialogue system. Previous studies exploited the historical dialogue information by attention-based graph structure simulation

but these methods cannot explicitly take advantage of the structure of the dialogue state. In addition

how to generate complex format dialogue states also brings challenges to research. In this paper

we propose a state memory graph network(SMGN). The network saves historical information through the state memory graph

and uses the graph to interact with the current dialogue. We also implement a complex dialogue state generation method based on state memory graph. Experimental results show that the proposed method improves the joint accuracy by 1.39% on the CrossWOZ dataset and 1.86% on the MultiWOZ dataset.

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references

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