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%0 Conference Proceedings
%T A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators
%A Thakur, Gautam
%A Caspersen, Janna
%A Herrmannova, Drahomira
%A Eaton, Bryan
%A Burdette, Jordan
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F thakur-etal-2021-mixed
%R 10.18653/v1/2021.findings-acl.169
%U https://aclanthology.org/2021.findings-acl.169
%U https://doi.org/10.18653/v1/2021.findings-acl.169
%P 1930-1938

Markdown (Informal)

[A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators](https://aclanthology.org/2021.findings-acl.169) (Thakur et al., Findings 2021)

• A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators (Thakur et al., Findings 2021)

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.