  The structured representation for semantic parsing in task-oriented assistant
systems is geared towards simple understanding of one-turn queries. Due to the
limitations of the representation, the session-based properties such as
co-reference resolution and context carryover are processed downstream in a
pipelined system. In this paper, we propose a semantic representation for such
task-oriented conversational systems that can represent concepts such as
co-reference and context carryover, enabling comprehensive understanding of
queries in a session. We release a new session-based, compositional
task-oriented parsing dataset of 20k sessions consisting of 60k utterances.
Unlike Dialog State Tracking Challenges, the queries in the dataset have
compositional forms. We propose a new family of Seq2Seq models for the
session-based parsing above, which achieve better or comparable performance to
the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2. Notably, we improve
the best known results on DSTC2 by up to 5 points for slot-carryover.
