Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
Open accessAbstract
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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Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems
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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
- hybrid
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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