Christopher M. Homan

Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
Findings from the Bambara - French Machine Translation Competition (BFMT 2023)
Vicarious Offense and Noise Audit of Offensive Speech Classifiers
Improving Label Quality by Jointly Modeling Items and Annotators
Neighborhood-based pooling for population-level label distribution learning

Published with Wowchemy — the free, open source website builder that empowers creators.