  We present a novel approach to dialogue state tracking and referring
expression resolution tasks. Successful contextual understanding of multi-turn
spoken dialogues requires resolving referring expressions across turns and
tracking the entities relevant to the conversation across turns. Tracking
conversational state is particularly challenging in a multi-domain scenario
when there exist multiple spoken language understanding (SLU) sub-systems, and
each SLU sub-system operates on its domain-specific meaning representation.
While previous approaches have addressed the disparate schema issue by learning
candidate transformations of the meaning representation, in this paper, we
instead model the reference resolution as a dialogue context-aware user query
reformulation task -- the dialog state is serialized to a sequence of natural
language tokens representing the conversation. We develop our model for query
reformulation using a pointer-generator network and a novel multi-task learning
setup. In our experiments, we show a significant improvement in absolute F1 on
an internal as well as a, soon to be released, public benchmark respectively.
