Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL

Haoyan Liu, Lei Fang, Qian Liu, Bei Chen, Jian-Guang Lou, Zhoujun Li

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.
Anthology ID:
D19-1356
Volume:
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:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3515–3520
Language:
URL:
https://aclanthology.org/D19-1356
DOI:
10.18653/v1/D19-1356
Bibkey:
Cite (ACL):
Haoyan Liu, Lei Fang, Qian Liu, Bei Chen, Jian-Guang Lou, and Zhoujun Li. 2019. Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL. 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 3515–3520, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (Liu et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/ingestion-script-update/D19-1356.pdf
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