A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators

Gautam Thakur, Janna Caspersen, Drahomira Herrmannova, Bryan Eaton, Jordan Burdette


Anthology ID:
2021.findings-acl.169
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:
1930–1938
Language:
URL:
https://aclanthology.org/2021.findings-acl.169
DOI:
10.18653/v1/2021.findings-acl.169
Bibkey:
Cite (ACL):
Gautam Thakur, Janna Caspersen, Drahomira Herrmannova, Bryan Eaton, and Jordan Burdette. 2021. A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1930–1938, Online. Association for Computational Linguistics.
Cite (Informal):
A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators (Thakur et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-acl.169.pdf