  Slot filling is a fundamental task in dialog state tracking in task-oriented
dialog systems. In multi-domain task-oriented dialog system, user utterances
and system responses may mention multiple named entities and attributes values.
A system needs to select those that are confirmed by the user and fill them
into destined slots. One difficulty is that since a dialogue session contains
multiple system-user turns, feeding in all the tokens into a deep model such as
BERT can be challenging due to limited capacity of input word tokens and GPU
memory. In this paper, we investigate an ontology-enhanced approach by matching
the named entities occurred in all dialogue turns using ontology. The matched
entities in the previous dialogue turns will be accumulated and encoded as
additional inputs to a BERT-based dialogue state tracker. In addition, our
improvement includes ontology constraint checking and the correction of slot
name tokenization. Experimental results showed that our ontology-enhanced
dialogue state tracker improves the joint goal accuracy (slot F1) from 52.63%
(91.64%) to 53.91% (92%) on MultiWOZ 2.1 corpus.
