Ensembling pretrained language models with varied layers and anisotropy removal, then taking the standard deviation of their continuous relatedness scores, predicts human annotator disagreement in the CoMeDi shared task better than discrete-label aggregation metrics.
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JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical Semantics Disagreements
Ensembling pretrained language models with varied layers and anisotropy removal, then taking the standard deviation of their continuous relatedness scores, predicts human annotator disagreement in the CoMeDi shared task better than discrete-label aggregation metrics.