  Dialogue State Tracking is central to multi-domain task-oriented dialogue
systems, responsible for extracting information from user utterances. We
present a novel hybrid architecture that augments GPT-2 with representations
derived from Graph Attention Networks in such a way to allow causal, sequential
prediction of slot values. The model architecture captures inter-slot
relationships and dependencies across domains that otherwise can be lost in
sequential prediction. We report improvements in state tracking performance in
MultiWOZ 2.0 against a strong GPT-2 baseline and investigate a simplified
sparse training scenario in which DST models are trained only on session-level
annotations but evaluated at the turn level. We further report detailed
analyses to demonstrate the effectiveness of graph models in DST by showing
that the proposed graph modules capture inter-slot dependencies and improve the
predictions of values that are common to multiple domains.
