Scaling Multi Domain Dialogue State Tracking via Query Reformulation
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.
NAACL