Gated Attentive Convolutional Network Dialogue State Tracker

In task-oriented dialogue systems, dialogue state tracking (DST) is an essential part which aims to estimate user goal at every step of the dialogue. At each turn, DST aims to estimate user goals by current user utterance and last system action. However, most current approaches encode these relevant sequences by recurrent networks which is a challenge for these models to capture long-range dependencies. Besides predicting the current dialogue state is relevant to historical context and sometimes refers to the past utterances. In this paper, we propose the Gated Attentive Convolutional network Dialogue State Tracker (GAC) which overcomes these challenges by utilizing the gated attentive convolutional encoder and introducing historical information. We use a gated attentive convolutional network encoder to learn the sequence representation and introduce historical dialogue utterances as evidence to track the user goal. Experiments show that our method improves joint goal accuracy on WoZ 2.0 and MultiWoZ 2.0 datasets respectively outperforms the previous state-of-the-art GLAD and Trade model.