  This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue
State Tracking. The goal of this task is to predict the intents and slots in
each user turn to complete the dialogue state tracking (DST) based on the
information provided by the task's schema. Different from traditional
stage-wise DST, we propose an end-to-end DST system to avoid error accumulation
between the dialogue turns. The DST system consists of a machine reading
comprehension (MRC) model for non-categorical slots and a Wide & Deep model for
categorical slots. As far as we know, this is the first time that MRC and Wide
& Deep model are applied to DST problem in a fully end-to-end way. Experimental
results show that our framework achieves an excellent performance on the test
dataset including 50% zero-shot services with a joint goal accuracy of 0.8652
and a slot tagging F1-Score of 0.9835.
