Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing-Reference-Cited by-同舟云学术

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Lihan¹,Qin Bowen¹,Hui Binyuan²,Li Bowen²,Yang Min³,Wang Bailin⁴,Li Binhua²,Sun Jian²,Huang Fei²,Si Luo²,Li Yongbin²

Affiliation:

1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Shenzhen, China

2. Alibaba Group, Beijing, China

3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

4. Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

Shenzhen Basic Research Foundation

Shenzhen Science and Technology Innovation Program

Youth Innovation Promotion Association of CAS China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539305

Reference58 articles.

1. Jimmy Lei Ba , Jamie Ryan Kiros, and Geoffrey E Hinton . 2016 . Layer normalization. arXiv preprint (2016). Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint (2016).

2. Yonatan Belinkov Nadir Durrani Fahim Dalvi Hassan Sajjad and James Glass. 2017. "What do Neural Machine Translation Models Learn about Morphology?. In ACL. Yonatan Belinkov Nadir Durrani Fahim Dalvi Hassan Sajjad and James Glass. 2017. "What do Neural Machine Translation Models Learn about Morphology?. In ACL.

3. Ben Bogin Jonathan Berant and Matt Gardner. 2019. Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing. In ACL. Ben Bogin Jonathan Berant and Matt Gardner. 2019. Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing. In ACL.

4. Michael M Bronstein , Joan Bruna , Yann LeCun , Arthur Szlam , and Pierre Vandergheynst . 2017. Geometric deep learning: going beyond euclidean data . In IEEE Signal Processing Magazine . Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric deep learning: going beyond euclidean data. In IEEE Signal Processing Magazine.

5. Ruisheng Cao , Lu Chen , Zhi Chen , Yanbin Zhao , Su Zhu , and Kai Yu . 2021 . LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non- Local Relations. In ACL. Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, and Kai Yu. 2021. LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non- Local Relations. In ACL.

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