%0 Conference Proceedings %T Conversational Semantic Parsing %A Aghajanyan, Armen %A Maillard, Jean %A Shrivastava, Akshat %A Diedrick, Keith %A Haeger, Michael %A Li, Haoran %A Mehdad, Yashar %A Stoyanov, Veselin %A Kumar, Anuj %A Lewis, Mike %A Gupta, Sonal %Y Webber, Bonnie %Y Cohn, Trevor %Y He, Yulan %Y Liu, Yang %S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) %D 2020 %8 November %I Association for Computational Linguistics %C Online %F aghajanyan-etal-2020-conversational %X 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 also set state-of-the-art in ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover. %R 10.18653/v1/2020.emnlp-main.408 %U https://aclanthology.org/2020.emnlp-main.408 %U https://doi.org/10.18653/v1/2020.emnlp-main.408 %P 5026-5035