Debiased Label Aggregation for Subjective Crowdsourcing Tasks

Author:

Wallace Shaun1,Cai Tianyuan2,Le Brendan1,Leiva Luis A.3

Affiliation:

1. Computer Science, Brown University, United States

2. Adobe, United States

3. University of Luxembourg, Luxembourg

Publisher

ACM

Reference86 articles.

1. Lora Aroyo , Anca Dumitrache , Oana Inel , Zoltán Szlávik , Benjamin Timmermans , and Chris Welty . 2019 . Crowdsourcing Inclusivity: Dealing with Diversity of Opinions, Perspectives and Ambiguity in Annotated Data . In Proc. WWW Companion. 1294–1295 . Lora Aroyo, Anca Dumitrache, Oana Inel, Zoltán Szlávik, Benjamin Timmermans, and Chris Welty. 2019. Crowdsourcing Inclusivity: Dealing with Diversity of Opinions, Perspectives and Ambiguity in Annotated Data. In Proc. WWW Companion. 1294–1295.

2. Agathe Balayn and Alessandro Bozzon . 2019 . Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity. In REAIS Workshop a HCOMP. Agathe Balayn and Alessandro Bozzon. 2019. Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity. In REAIS Workshop a HCOMP.

3. Rewriting a Deep Generative Model

4. Sprout

5. Systematic Labeling Bias: De-biasing Where Everyone is Wrong

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1. Automated ISAR Image Quality Assessment;2023 IEEE International Radar Conference (RADAR);2023-11-06

2. Crowdsourcing Subjective Annotations Using Pairwise Comparisons Reduces Bias and Error Compared to the Majority-vote Method;Proceedings of the ACM on Human-Computer Interaction;2023-09-28

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