Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
Proceedings of the AAAI conference on human computation and crowdsourcing , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Proposes and illustrates a community-informed, multi-perspective approach to developing AI for analyzing LAPD body-worn camera footage of traffic stops.
citing papers explorer
-
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.
-
Community-Informed AI Models for Police Accountability
Proposes and illustrates a community-informed, multi-perspective approach to developing AI for analyzing LAPD body-worn camera footage of traffic stops.