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

Bo Chen, Le Sun, Xianpei Han


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.
Anthology ID:
P18-1071
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
766–777
Language:
URL:
https://aclanthology.org/P18-1071
DOI:
10.18653/v1/P18-1071
Bibkey:
Cite (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.
Cite (Informal):
Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (Chen et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1071.pdf
Presentation:
 P18-1071.Presentation.pdf
Video:
 https://vimeo.com/285800915
Code
 dongpobeyond/Seq2Act