  Dialogue State Tracking is a crucial part of multi-domain task-oriented
dialogue systems, responsible for extracting information from user utterances.
We present a novel architecture that utilizes the powerful generative model
GPT-2 to generate slot values one by one causally, and at the same time
utilizes Graph Attention Networks to enable inter-slot information exchanges,
which exploits the inter-slot relations such as correlations. Our model
achieves $54.86\%$ joint accuracy in MultiWOZ 2.0, and it retains a performance
of up to $50.43\%$ in sparse supervision training, where only session-level
annotations ($14.3\%$ of the full training set) are used. We conduct detailed
analyses to demonstrate the significance of using graph models in this task,
and show by experiments that the proposed graph modules indeed help to capture
more inter-slot relations.
