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

Sadat Shahriar, T. Solorio
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引用次数: 1

Abstract

Subjectivity and difference of opinion are key social phenomena, and it is crucial to take these into account in the annotation and detection process of derogatory textual content. In this paper, we use four datasets provided by SemEval-2023 Task 11 and fine-tune a BERT model to capture the disagreement in the annotation. We find individual annotator modeling and aggregation lowers the Cross-Entropy score by an average of 0.21, compared to the direct training on the soft labels. Our findings further demonstrate that annotator metadata contributes to the average 0.029 reduction in the Cross-Entropy score.
safewebh在semevale -2023的任务11:学习贬义文本中的注释者分歧:直接训练与聚合的比较
主观性和意见分歧是关键的社会现象,在贬义文本内容的标注和检测过程中,考虑到这一点至关重要。在本文中,我们使用SemEval-2023 Task 11提供的四个数据集,并对BERT模型进行微调,以捕获注释中的分歧。我们发现,与在软标签上直接训练相比,单个注释器建模和聚合使交叉熵得分平均降低了0.21。我们的研究结果进一步表明,注释者元数据使交叉熵得分平均降低了0.029。
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