@inproceedings{pasupat-etal-2019-span,
title = "Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog",
author = "Pasupat, Panupong and
Gupta, Sonal and
Mandyam, Karishma and
Shah, Rushin and
Lewis, Mike and
Zettlemoyer, Luke",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1163",
doi = "10.18653/v1/D19-1163",
pages = "1520--1526",
abstract = "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.",
}
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<abstract>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.</abstract>
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%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
Markdown (Informal)
[Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog](https://aclanthology.org/D19-1163) (Pasupat et al., EMNLP-IJCNLP 2019)
ACL
- Panupong Pasupat, Sonal Gupta, Karishma Mandyam, Rushin Shah, Mike Lewis, and Luke Zettlemoyer. 2019. Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1520–1526, Hong Kong, China. Association for Computational Linguistics.