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Subjectivity and difference of opinion are key social phenomena, and it iscrucial to take these into account in the annotation and detection process ofderogatory textual content. In this paper, we use four datasets provided bySemEval-2023 Task 11 and fine-tune a BERT model to capture the disagreement inthe annotation. We find individual annotator modeling and aggregation lowersthe Cross-Entropy score by an average of 0.21, compared to the direct trainingon the soft labels. Our findings further demonstrate that annotator metadatacontributes to the average 0.029 reduction in the Cross-Entropy score.

SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation