  Dialogue state tracking (DST) aims at estimating the current dialogue state
given all the preceding conversation. For multi-domain DST, the data sparsity
problem is a major obstacle due to increased numbers of state candidates and
dialogue lengths. To encode the dialogue context efficiently, we utilize the
previous dialogue state (predicted) and the current dialogue utterance as the
input for DST. To consider relations among different domain-slots, the schema
graph involving prior knowledge is exploited. In this paper, a novel context
and schema fusion network is proposed to encode the dialogue context and schema
graph by using internal and external attention mechanisms. Experiment results
show that our approach can obtain new state-of-the-art performance of the
open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.
