建模不同标注者道德观，让文本道德分类更贴近真实多元判断。
Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts
- 为预训练模型添加标注者专属特征层，捕捉个体道德视角差异。
- 在短文本上预测单个标注者意见的准确率显著提升。
- 揭示标注者道德立场差异，适合研究社会舆论与价值分歧的学者。
理解社交媒体文本中的道德价值观有助于洞察道德判断形成机制。尽管基于众包数据的监督NLP模型已取得良好分类性能，但多数方法将多个标注者的标签合并为单一“真值”，忽视了任务的内在主观性。实际中，标注者间分歧常源于个人观点或文本本身的模糊性，尤其在短文本（如推文）上更为明显。本文在预训练语言模型基础上增加一层，学习标注者特定的特征。该模型不仅提升了对个体标注结果的预测能力，还生成可揭示标注者道德视角的表示。我们发现，基于聚合标签训练的模型可能掩盖多样性，导致性能评估产生误导。总体而言，分歧反映了任务的主观本质，建模个体视角能显著提升文本道德分类的效果。
原文摘要 · Abstract (English)
Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classification performance. However, most approaches simplify the problem by aggregating multiple annotators' labels into a single "ground truth", overlooking the inherent subjectivity of the task. In practice, there are disagreements between annotators caused by personal viewpoint or inherent ambiguities, particularly for short tweets. Here, we extend a pretrained language model with a layer that learns annotator-specific features. Our model improves predictions of individual annotations and yields representations that reveal meaningful insights into annotators' moral perspectives. We show that models trained on aggregated labels may hide variation and give a misleading impression of performance. Overall, we demonstrate that disagreement reflects the inherent subjectivity of the task and that modelling individual perspectives creates benefits for moral classification of texts.
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