@inproceedings{liu-etal-2019-leveraging,
title = "Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-{SQL}",
author = "Liu, Haoyan and
Fang, Lei and
Liu, Qian and
Chen, Bei and
Lou, Jian-Guang and
Li, Zhoujun",
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/D19-1356",
doi = "10.18653/v1/D19-1356",
pages = "3515--3520",
abstract = "One key component in text-to-SQL is to predict the comparison relations between columns and their values. To the best of our knowledge, no existing models explicitly introduce external common knowledge to address this problem, thus their capabilities of predicting comparison relations are limited beyond training data. In this paper, we propose to leverage adjective-noun phrasing knowledge mined from the web to predict the comparison relations in text-to-SQL. Experimental results on both the original and the re-split Spider dataset show that our approach achieves significant improvement over state-of-the-art methods on comparison relation prediction.",
}
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<abstract>One key component in text-to-SQL is to predict the comparison relations between columns and their values. To the best of our knowledge, no existing models explicitly introduce external common knowledge to address this problem, thus their capabilities of predicting comparison relations are limited beyond training data. In this paper, we propose to leverage adjective-noun phrasing knowledge mined from the web to predict the comparison relations in text-to-SQL. Experimental results on both the original and the re-split Spider dataset show that our approach achieves significant improvement over state-of-the-art methods on comparison relation prediction.</abstract>
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%0 Conference Proceedings
%T Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL
%A Liu, Haoyan
%A Fang, Lei
%A Liu, Qian
%A Chen, Bei
%A Lou, Jian-Guang
%A Li, Zhoujun
%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 liu-etal-2019-leveraging
%X One key component in text-to-SQL is to predict the comparison relations between columns and their values. To the best of our knowledge, no existing models explicitly introduce external common knowledge to address this problem, thus their capabilities of predicting comparison relations are limited beyond training data. In this paper, we propose to leverage adjective-noun phrasing knowledge mined from the web to predict the comparison relations in text-to-SQL. Experimental results on both the original and the re-split Spider dataset show that our approach achieves significant improvement over state-of-the-art methods on comparison relation prediction.
%R 10.18653/v1/D19-1356
%U https://aclanthology.org/D19-1356
%U https://doi.org/10.18653/v1/D19-1356
%P 3515-3520
Markdown (Informal)
[Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL](https://aclanthology.org/D19-1356) (Liu et al., EMNLP-IJCNLP 2019)
ACL