  We consider a new perspective on dialog state tracking (DST), the task of
estimating a user's goal through the course of a dialog. By formulating DST as
a semantic parsing task over hierarchical representations, we can incorporate
semantic compositionality, cross-domain knowledge sharing and co-reference. We
present TreeDST, a dataset of 27k conversations annotated with tree-structured
dialog states and system acts. We describe an encoder-decoder framework for DST
with hierarchical representations, which leads to 20% improvement over
state-of-the-art DST approaches that operate on a flat meaning space of
slot-value pairs.
