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
- Editors:
- Iryna Gurevych, Yusuke Miyao
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P18-1071.pdf
- Code
- dongpobeyond/Seq2Act