%0 Conference Proceedings %T Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog %A Pasupat, Panupong %A Gupta, Sonal %A Mandyam, Karishma %A Shah, Rushin %A Lewis, Mike %A Zettlemoyer, Luke %Y Inui, Kentaro %Y Jiang, Jing %Y Ng, Vincent %Y Wan, Xiaojun %S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong, China %F pasupat-etal-2019-span %X We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally. In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy. Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed. %R 10.18653/v1/D19-1163 %U https://aclanthology.org/D19-1163 %U https://doi.org/10.18653/v1/D19-1163 %P 1520-1526