Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning

Benjamin Minixhofer, Milan Gritta, Ignacio Iacobacci


Anthology ID:
2021.findings-acl.26
Volume:
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Month:
August
Year:
2021
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
303–313
Language:
URL:
https://aclanthology.org/2021.findings-acl.26
DOI:
10.18653/v1/2021.findings-acl.26
Bibkey:
Cite (ACL):
Benjamin Minixhofer, Milan Gritta, and Ignacio Iacobacci. 2021. Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 303–313, Online. Association for Computational Linguistics.
Cite (Informal):
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning (Minixhofer et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-acl.26.pdf
Video:
 https://aclanthology.org/2021.findings-acl.26.mp4
Code
 huawei-noah/noah-research
Data
ANLIGLUEMultiNLIQNLISNLISuperGLUE