Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL
Octavian Popescu, Irene Manotas, Ngoc Phuoc An Vo, Hangu Yeo, Elahe Khorashani, Vadim Sheinin
Correct Metadata for
Abstract
Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases. For the popular Spider dataset, despite models achieving 70% accuracy on its development or test sets, the same models show a huge decline below 20% accuracy for unseen databases. The root causes for this problem are complex and they cannot be easily fixed by adding more manually created training. In this paper we address the problem and propose a solution that is a hybrid system using automated training-data augmentation technique. Our system consists of a rule-based and a deep learning components that interact to understand crucial information in a given query and produce correct SQL as a result. It achieves double-digit percentage improvement for databases that are not part of the Spider corpus.- Anthology ID:
- 2022.coling-1.137
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1593–1603
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.137/
- DOI:
- Bibkey:
- Cite (ACL):
- Octavian Popescu, Irene Manotas, Ngoc Phuoc An Vo, Hangu Yeo, Elahe Khorashani, and Vadim Sheinin. 2022. Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1593–1603, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL (Popescu et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.137.pdf
- Data
- Spider-Realistic
Export citation
@inproceedings{popescu-etal-2022-addressing,
title = "Addressing Limitations of Encoder-Decoder Based Approach to Text-to-{SQL}",
author = "Popescu, Octavian and
Manotas, Irene and
Vo, Ngoc Phuoc An and
Yeo, Hangu and
Khorashani, Elahe and
Sheinin, Vadim",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.137/",
pages = "1593--1603",
abstract = "Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases. For the popular Spider dataset, despite models achieving 70{\%} accuracy on its development or test sets, the same models show a huge decline below 20{\%} accuracy for unseen databases. The root causes for this problem are complex and they cannot be easily fixed by adding more manually created training. In this paper we address the problem and propose a solution that is a hybrid system using automated training-data augmentation technique. Our system consists of a rule-based and a deep learning components that interact to understand crucial information in a given query and produce correct SQL as a result. It achieves double-digit percentage improvement for databases that are not part of the Spider corpus."
}
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%0 Conference Proceedings %T Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL %A Popescu, Octavian %A Manotas, Irene %A Vo, Ngoc Phuoc An %A Yeo, Hangu %A Khorashani, Elahe %A Sheinin, Vadim %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F popescu-etal-2022-addressing %X Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases. For the popular Spider dataset, despite models achieving 70% accuracy on its development or test sets, the same models show a huge decline below 20% accuracy for unseen databases. The root causes for this problem are complex and they cannot be easily fixed by adding more manually created training. In this paper we address the problem and propose a solution that is a hybrid system using automated training-data augmentation technique. Our system consists of a rule-based and a deep learning components that interact to understand crucial information in a given query and produce correct SQL as a result. It achieves double-digit percentage improvement for databases that are not part of the Spider corpus. %U https://aclanthology.org/2022.coling-1.137/ %P 1593-1603
Markdown (Informal)
[Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL](https://aclanthology.org/2022.coling-1.137/) (Popescu et al., COLING 2022)
- Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL (Popescu et al., COLING 2022)
ACL
- Octavian Popescu, Irene Manotas, Ngoc Phuoc An Vo, Hangu Yeo, Elahe Khorashani, and Vadim Sheinin. 2022. Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1593–1603, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.