Cited by 69Natural Language Processing TechniquesTopic ModelingSpeech and dialogue systems

Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

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Chen, Bo, Sun, Le, Han, Xianpei·2018·Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Abstract

This paper proposes a neural semantic parsing approach -Sequence-to-Action, which models semantic parsing as an endto-end semantic graph generation process.Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing.Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases.Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation.Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.

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Research works printed but not linked: 3

  1. Jonathan Berant and Percy Liang. 2014. Se- mantic parsing via paraphrasing. In Proceed- ings of the 52nd Annual Meeting of the Asso- ciation for Computational Linguistics (Volume 1: Long Papers). Association for Computational Lin- guistics, Baltimore, Maryland, pages 1415-1425. http://www.aclweb.org/anthology/P14-1133.
  2. Tom Kwiatkowski, Luke Zettlemoyer, Sharon Gold- water, and Mark Steedman. 2011. Lexical gen- eralization in ccg grammar induction for seman- tic parsing. In Proceedings of the 2011 Confer- ence on Empirical Methods in Natural Language Processing. Association for Computational Linguis- tics, Edinburgh, Scotland, UK., pages 1512-1523. http://www.aclweb.org/anthology/D11-1140.
  3. Hoifung Poon. 2013. Grounded unsupervised seman- tic parsing. In Proceedings of the 51st Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computa- tional Linguistics, Sofia, Bulgaria, pages 933-943. http://www.aclweb.org/anthology/P13-1092.

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Authors
Chen, B., Sun, L., & Han, X.
Affiliations
Chinese Academy of Sciences (CN)
Venue
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Published
2018
Pages
766-777
ISSN
0736-587X (ISSN-L)
OpenAlex
W2798296074
Cited by
69
References
52
Citations by year
2026: 1 · 2025: 6 · 2024: 2 · 2023: 6 · 2022: 8 · 2021: 13 · 2020: 20 · 2019: 11 · 2018: 2
FWCI
4.70
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (61772505, 61572477)
  • National Key Research and Development Program of China (2017YFB1002104, No.2017YFB1002104)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
semantic parsing, knowledge base, recurrent neural networks, GEO datasets, ATIS dataset

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