  We investigate the problem of multi-domain Dialogue State Tracking (DST) with
open vocabulary, which aims to extract the state from the dialogue. Existing
approaches usually concatenate previous dialogue state with dialogue history as
the input to a bi-directional Transformer encoder. They rely on the
self-attention mechanism of Transformer to connect tokens in them. However,
attention may be paid to spurious connections, leading to wrong inference. In
this paper, we propose to construct a dialogue state graph in which domains,
slots and values from the previous dialogue state are connected properly.
Through training, the graph node and edge embeddings can encode co-occurrence
relations between domain-domain, slot-slot and domain-slot, reflecting the
strong transition paths in general dialogue. The state graph, encoded with
relational-GCN, is fused into the Transformer encoder. Experimental results
show that our approach achieves a new state of the art on the task while
remaining efficient. It outperforms existing open-vocabulary DST approaches.
