  As an essential component in task-oriented dialogue systems, dialogue state
tracking (DST) aims to track human-machine interactions and generate state
representations for managing the dialogue. Representations of dialogue states
are dependent on the domain ontology and the user's goals. In several
task-oriented dialogues with a limited scope of objectives, dialogue states can
be represented as a set of slot-value pairs. As the capabilities of dialogue
systems expand to support increasing naturalness in communication,
incorporating dialogue act processing into dialogue model design becomes
essential. The lack of such consideration limits the scalability of dialogue
state tracking models for dialogues having specific objectives and ontology. To
address this issue, we formulate and incorporate dialogue acts, and leverage
recent advances in machine reading comprehension to predict both categorical
and non-categorical types of slots for multi-domain dialogue state tracking.
Experimental results show that our models can improve the overall accuracy of
dialogue state tracking on the MultiWOZ 2.1 dataset, and demonstrate that
incorporating dialogue acts can guide dialogue state design for future
task-oriented dialogue systems.
