SMGN：用于对话状态跟踪的状态记忆图网络

张志昌; 于沛霖; 庞雅丽; 朱林; 曾扬扬

doi:10.12263/DZXB.20201463

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SMGN：用于对话状态跟踪的状态记忆图网络

学术论文 | 更新时间：2025-12-08

- SMGN：用于对话状态跟踪的状态记忆图网络
- SMGN: A State Memory Graph Network for Dialogue State Tracking
- 电子学报 2022年50卷第8期页码：1851-1858
- 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：
  
  [ "张志昌男，1976年4月出生，甘肃天水人.教授、硕士生导师.1998年、2003年和2010年分别在西北师范大学、西北工业大学、哈尔滨工业大学获工学学士、工学硕士和工学博士学位.研究方向为自然语言处理，主要进行问答技术、医疗文本处理技术研究.E-mail: zzc@nwnu.edu.cn" ]
  [ "于沛霖男，1996年1月出生，甘肃兰州人.西北师范大学计算机科学与工程学院硕士研究生.研究方向为自然语言处理.E-mail: ypl670335924@gmail.com" ]
- 基金信息：
  
  国家自然科学基金(61762081);甘肃省重点研发计划项目(17YF1GA016)
- DOI：10.12263/DZXB.20201463
  中图分类号： TP391.1;
- 收稿：2020-12-18，
  
  修回：2021-04-15，
  
  纸质出版：2022-08-25
- 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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