  Most recently proposed approaches in dialogue state tracking (DST) leverage
the context and the last dialogue states to track current dialogue states,
which are often slot-value pairs. Although the context contains the complete
dialogue information, the information is usually indirect and even requires
reasoning to obtain. The information in the lastly predicted dialogue states is
direct, but when there is a prediction error, the dialogue information from
this source will be incomplete or erroneous. In this paper, we propose the
Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States
and Conversations network (FPDSC). This model extracts information of each
dialogue turn by modeling interactions among each turn utterance, the
corresponding last dialogue states, and dialogue slots. Then the representation
of each dialogue turn is aggregated by a hierarchical structure to form the
passage information, which is utilized in the current turn of DST. Experimental
results validate the effectiveness of the fusion network with 55.03% and 59.07%
joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets, which reaches the
state-of-the-art performance. Furthermore, we conduct the deleted-value and
related-slot experiments on MultiWOZ 2.1 to evaluate our model.
