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

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%.

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.