SQL-to-Text Generation with Graph-to-Sequence Model

Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin


Abstract
Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we propose a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.
Anthology ID:
D18-1112
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
931–936
Language:
URL:
https://aclanthology.org/D18-1112
DOI:
10.18653/v1/D18-1112
Bibkey:
Cite (ACL):
Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, and Vadim Sheinin. 2018. SQL-to-Text Generation with Graph-to-Sequence Model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 931–936, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
SQL-to-Text Generation with Graph-to-Sequence Model (Xu et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1112.pdf
Video:
 https://vimeo.com/305213739
Code
 IBM/SQL-to-Text
Data
WikiSQL