  In task-oriented dialogue systems the dialogue state tracker (DST) component
is responsible for predicting the state of the dialogue based on the dialogue
history. Current DST approaches rely on a predefined domain ontology, a fact
that limits their effective usage for large scale conversational agents, where
the DST constantly needs to be interfaced with ever-increasing services and
APIs. Focused towards overcoming this drawback, we propose a domain-aware
dialogue state tracker, that is completely data-driven and it is modeled to
predict for dynamic service schemas. The proposed model utilizes domain and
slot information to extract both domain and slot specific representations for a
given dialogue, and then uses such representations to predict the values of the
corresponding slot. Integrating this mechanism with a pretrained language model
(i.e. BERT), our approach can effectively learn semantic relations.
