@inproceedings{zhang-etal-2019-editing,
title = "Editing-Based {SQL} Query Generation for Cross-Domain Context-Dependent Questions",
author = "Zhang, Rui and
Yu, Tao and
Er, Heyang and
Shim, Sungrok and
Xue, Eric and
Lin, Xi Victoria and
Shi, Tianze and
Xiong, Caiming and
Socher, Richard and
Radev, Dragomir",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1537",
doi = "10.18653/v1/D19-1537",
pages = "5338--5349",
abstract = "We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at \url{https://github.com/ryanzhumich/sparc_atis_pytorch}.",
}
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<abstract>We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.</abstract>
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%0 Conference Proceedings
%T Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
%A Zhang, Rui
%A Yu, Tao
%A Er, Heyang
%A Shim, Sungrok
%A Xue, Eric
%A Lin, Xi Victoria
%A Shi, Tianze
%A Xiong, Caiming
%A Socher, Richard
%A Radev, Dragomir
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhang-etal-2019-editing
%X We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.
%R 10.18653/v1/D19-1537
%U https://aclanthology.org/D19-1537
%U https://doi.org/10.18653/v1/D19-1537
%P 5338-5349
Markdown (Informal)
[Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://aclanthology.org/D19-1537) (Zhang et al., EMNLP-IJCNLP 2019)
ACL
- Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, and Dragomir Radev. 2019. Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5338–5349, Hong Kong, China. Association for Computational Linguistics.