A framework jointly models annotator-specific NLI labels and explanations using conditioned representations and two explainer architectures, improving predictive performance over baselines.
Toward a perspectivist turn in ground truthing for predictive computing
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
Large-scale statistical analysis of four harmful language datasets reveals that interactions between annotator characteristics and linguistic cues drive annotation variation, with lexical features and attitudes prominent but patterns varying by dataset.
Multi-level bootstrapping models annotator variance using large rater-ID datasets to find optimal tradeoffs between number of items N and ratings per item K for statistically significant AI evaluations.
citing papers explorer
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Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
A framework jointly models annotator-specific NLI labels and explanations using conditioned representations and two explainer architectures, improving predictive performance over baselines.
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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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Who and What? Using Linguistic Features and Annotator Characteristics to Analyze Annotation Variation
Large-scale statistical analysis of four harmful language datasets reveals that interactions between annotator characteristics and linguistic cues drive annotation variation, with lexical features and attitudes prominent but patterns varying by dataset.
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Improving Reproducibility in Evaluation through Multi-Level Annotator Modeling
Multi-level bootstrapping models annotator variance using large rater-ID datasets to find optimal tradeoffs between number of items N and ratings per item K for statistically significant AI evaluations.