@inproceedings{cai-wan-2020-igsql,
title = "{IGSQL}: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-{SQL} Generation",
author = "Cai, Yitao and
Wan, Xiaojun",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.560",
doi = "10.18653/v1/2020.emnlp-main.560",
pages = "6903--6912",
abstract = "Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historic user inputs. In this work, in addition to using encoders to capture historic information of user inputs, we propose a database schema interaction graph encoder to utilize historic information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.",
}
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%0 Conference Proceedings
%T IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
%A Cai, Yitao
%A Wan, Xiaojun
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cai-wan-2020-igsql
%X Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historic user inputs. In this work, in addition to using encoders to capture historic information of user inputs, we propose a database schema interaction graph encoder to utilize historic information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.
%R 10.18653/v1/2020.emnlp-main.560
%U https://aclanthology.org/2020.emnlp-main.560
%U https://doi.org/10.18653/v1/2020.emnlp-main.560
%P 6903-6912
Markdown (Informal)
[IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation](https://aclanthology.org/2020.emnlp-main.560) (Cai & Wan, EMNLP 2020)
ACL