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        <title>Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing</title>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <abstract>This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-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.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1071</identifier>
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%0 Conference Proceedings
%T Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
%A Chen, Bo
%A Sun, Le
%A Han, Xianpei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F chen-etal-2018-sequence
%X This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-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.
%R 10.18653/v1/P18-1071
%U https://aclanthology.org/P18-1071
%U https://doi.org/10.18653/v1/P18-1071
%P 766-777

Markdown (Informal)

[Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing](https://aclanthology.org/P18-1071) (Chen et al., ACL 2018)

• Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (Chen et al., ACL 2018)

ACL

• Bo Chen, Le Sun, and Xianpei Han. 2018. Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 766–777, Melbourne, Australia. Association for Computational Linguistics.