Most recently proposed approaches in dialogue state tracking (DST) leverage the context and the last dialogue states to track current dialogue states, which are often slot-value pairs. Although the context contains the complete dialogue information, the information is usually indirect and even requires reasoning to obtain. The information in the lastly predicted dialogue states is direct, but when there is a prediction error, the dialogue information from this source will be incomplete or erroneous. In this paper, we propose the Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations network (FPDSC). This model extracts information of each dialogue turn by modeling interactions among each turn utterance, the corresponding last dialogue states, and dialogue slots. Then the representation of each dialogue turn is aggregated by a hierarchical structure to form the passage information, which is utilized in the current turn of DST. Experimental results validate the effectiveness of the fusion network with 55.03% and 59.07% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, which reaches the state-of-the-art performance. Furthermore, we conduct the deleted-value and related-slot experiments on MultiWOZ 2.1 to evaluate our model.

本论文提出了一种多层次融合对话状态跟踪（DST）网络，通过建模每个对话轮次的交互，实现从以往对话历史和当前对话上下文中抽取对话状态信息，进行预测和融合，加强对当前对话状态的跟踪和理解，实验结果表明该模型在MultiWOZ 2.0和MultiWOZ 2.1数据集上取得了当前领先的55.03%和59.07%联合灵敏度值。

预测对话状态和对话内容的多级融合对话状态跟踪