Label Distribution Learning | Tharindu Cyril Weerasooriya

Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo

Sat, 01 Jul 2023 00:00:00 +0000

Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning

Sat, 01 Jul 2023 00:00:00 +0000

Most AI training forces annotators to agree on a single “correct” label, erasing legitimate differences of opinion. But when humans disagree (like on whether a tweet is offensive), that disagreement is often meaningful—not noise. CrowdOpinion solves this by clustering similar content and predicting the full distribution of opinions people might have, rather than picking one “winner.” We tested it on Twitter, Reddit, Gab, and Facebook data, where disagreement is common. This approach ensures minority viewpoints aren’t silenced, which is crucial for building fair AI that respects diverse perspectives—especially important for content moderation and other subjective decisions.

Improving Label Quality by Joint Probabilistic Modeling of Items and Annotators

Sat, 01 Jan 2022 00:00:00 +0000

Improving Label Quality by Jointly Modeling Items and Annotators

Fri, 01 Jan 2021 00:00:00 +0000

Neighborhood-based pooling for population-level label distribution learning

Wed, 01 Jan 2020 00:00:00 +0000