DoT: An efficient Double Transformer for NLP tasks with tables

Syrine Krichene, Thomas Müller, Julian Eisenschlos


Anthology ID:
2021.findings-acl.289
Volume:
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3273–3283
Language:
URL:
https://aclanthology.org/2021.findings-acl.289
DOI:
10.18653/v1/2021.findings-acl.289
Bibkey:
Cite (ACL):
Syrine Krichene, Thomas Müller, and Julian Eisenschlos. 2021. DoT: An efficient Double Transformer for NLP tasks with tables. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3273–3283, Online. Association for Computational Linguistics.
Cite (Informal):
DoT: An efficient Double Transformer for NLP tasks with tables (Krichene et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-acl.289.pdf
Video:
 https://aclanthology.org/2021.findings-acl.289.mp4
Code
 google-research/tapas
Data
TabFactWikiSQL