GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics

Fengyu Cai, Xinran Zhao, Hongming Zhang, Iryna Gurevych, Heinz Koeppl


Anthology ID:
2024.findings-acl.332
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5571–5597
Language:
URL:
https://aclanthology.org/2024.findings-acl.332
DOI:
10.18653/v1/2024.findings-acl.332
Bibkey:
Cite (ACL):
Fengyu Cai, Xinran Zhao, Hongming Zhang, Iryna Gurevych, and Heinz Koeppl. 2024. GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5571–5597, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics (Cai et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-acl.332.pdf