@inproceedings{yu-etal-2019-sparc,
title = "{SP}ar{C}: Cross-Domain Semantic Parsing in Context",
author = "Yu, Tao and
Zhang, Rui and
Yasunaga, Michihiro and
Tan, Yi Chern and
Lin, Xi Victoria and
Li, Suyi and
Er, Heyang and
Li, Irene and
Pang, Bo and
Chen, Tao and
Ji, Emily and
Dixit, Shreya and
Proctor, David and
Shim, Sungrok and
Kraft, Jonathan and
Zhang, Vincent and
Xiong, Caiming and
Socher, Richard and
Radev, Dragomir",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1443",
doi = "10.18653/v1/P19-1443",
pages = "4511--4523",
abstract = "We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2{\%} over all questions and less than10{\%} over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at \url{https://yale-lily.github.io/sparc}.",
}
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<abstract>We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.</abstract>
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%0 Conference Proceedings
%T SParC: Cross-Domain Semantic Parsing in Context
%A Yu, Tao
%A Zhang, Rui
%A Yasunaga, Michihiro
%A Tan, Yi Chern
%A Lin, Xi Victoria
%A Li, Suyi
%A Er, Heyang
%A Li, Irene
%A Pang, Bo
%A Chen, Tao
%A Ji, Emily
%A Dixit, Shreya
%A Proctor, David
%A Shim, Sungrok
%A Kraft, Jonathan
%A Zhang, Vincent
%A Xiong, Caiming
%A Socher, Richard
%A Radev, Dragomir
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yu-etal-2019-sparc
%X We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.
%R 10.18653/v1/P19-1443
%U https://aclanthology.org/P19-1443
%U https://doi.org/10.18653/v1/P19-1443
%P 4511-4523
Markdown (Informal)
[SParC: Cross-Domain Semantic Parsing in Context](https://aclanthology.org/P19-1443) (Yu et al., ACL 2019)
ACL
- Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, and Dragomir Radev. 2019. SParC: Cross-Domain Semantic Parsing in Context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4511–4523, Florence, Italy. Association for Computational Linguistics.