@inproceedings{xu-etal-2018-sql,
title = "{SQL}-to-Text Generation with Graph-to-Sequence Model",
author = "Xu, Kun and
Wu, Lingfei and
Wang, Zhiguo and
Feng, Yansong and
Sheinin, Vadim",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1112",
doi = "10.18653/v1/D18-1112",
pages = "931--936",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SQL-to-Text Generation with Graph-to-Sequence Model
%A Xu, Kun
%A Wu, Lingfei
%A Wang, Zhiguo
%A Feng, Yansong
%A Sheinin, Vadim
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xu-etal-2018-sql
%X 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.
%R 10.18653/v1/D18-1112
%U https://aclanthology.org/D18-1112
%U https://doi.org/10.18653/v1/D18-1112
%P 931-936
Markdown (Informal)
[SQL-to-Text Generation with Graph-to-Sequence Model](https://aclanthology.org/D18-1112) (Xu et al., EMNLP 2018)
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.