Ensemble Diversity Optimization jointly learns ensemble weights, size, and a signed diversity regularizer, substantially improving calibration to annotator distributions on subjective text classification.
C onv A buse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI
3 Pith papers cite this work, alongside 38 external citations. Polarity classification is still indexing.
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Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
STABLEVAL produces stable AI system rankings by modeling latent correctness and annotator confusion rather than majority vote aggregation.
citing papers explorer
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Ensemble Diversity Optimization for Subjective Supervision
Ensemble Diversity Optimization jointly learns ensemble weights, size, and a signed diversity regularizer, substantially improving calibration to annotator distributions on subjective text classification.
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Beyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Tasks
Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
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STABLEVAL: Disagreement-Aware and Stable Evaluation of AI Systems
STABLEVAL produces stable AI system rankings by modeling latent correctness and annotator confusion rather than majority vote aggregation.