annotation disagreement

Everyone’s Voice Matters: Quantifying Annotation Disagreement Using Demographic Information AAAI 2023 MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement SemEval 2023 No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference ACL 2023 SemEval-2023 Task 11: Learning with Disagreements (LeWiDi) SemEval 2023 Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning ACL 2023 Toward a Perspectivist Turn in Ground Truthing for Predictive Computing AAAI 2023 UMUTeam at SemEval-2023 Task 11: Ensemble Learning applied to Binary Supervised Classifiers with disagreements SemEval 2023 Identifying inherent disagreement in natural language inference NAACL 2021 SemEval-2021 Task 12: Learning with Disagreements SemEval 2021 We Need to Consider Disagreement in Evaluation ACL 2021 Identifying Annotator Bias: A new IRT-based method for bias identification COLING 2020 STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval EMNLP 2020 Sentiment Analysis: It’s Complicated! NAACL 2018 Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations CVPR 2016