@inproceedings{li-etal-2020-mean,
title = "{``}What Do You Mean by That?{''} A Parser-Independent Interactive Approach for Enhancing Text-to-{SQL}",
author = "Li, Yuntao and
Chen, Bei and
Liu, Qian and
Gao, Yan and
Lou, Jian-Guang and
Zhang, Yan and
Zhang, Dongmei",
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.561",
doi = "10.18653/v1/2020.emnlp-main.561",
pages = "6913--6922",
abstract = "In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users{'} natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.",
}
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<abstract>In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users’ natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.</abstract>
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%0 Conference Proceedings
%T “What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
%A Li, Yuntao
%A Chen, Bei
%A Liu, Qian
%A Gao, Yan
%A Lou, Jian-Guang
%A Zhang, Yan
%A Zhang, Dongmei
%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 li-etal-2020-mean
%X In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users’ natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.
%R 10.18653/v1/2020.emnlp-main.561
%U https://aclanthology.org/2020.emnlp-main.561
%U https://doi.org/10.18653/v1/2020.emnlp-main.561
%P 6913-6922
Markdown (Informal)
[“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL](https://aclanthology.org/2020.emnlp-main.561) (Li et al., EMNLP 2020)
ACL