Agreement-based clustering of annotators improves performance on subjective NLP tasks by capturing diverse perspectives better than majority voting or per-annotator modeling.
arXiv preprint arXiv:2201.08277 , year=
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The Meaning Intelligence Framework raises zero-shot register classification accuracy from 33.3% to 73.3% on a 30-item Nigerian discourse calibration set while showing that smaller models can outperform larger ones on cultural competence.
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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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The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
The Meaning Intelligence Framework raises zero-shot register classification accuracy from 33.3% to 73.3% on a 30-item Nigerian discourse calibration set while showing that smaller models can outperform larger ones on cultural competence.