用社会人口特征建模人类观点差异，提升标注视角预测准确率
Modeling Human Perspectives with Socio-Demographic Representations
- 通过对比学习联合建模标注者视角与社会人口特征
- 在多维度人口属性下显著优于传统拼接方法，提升视角预测性能
- 可解释性强，适合研究社会偏见、数据标注偏差等场景
人类对同一问题常持有不同观点。在众多自然语言处理任务中，标注分歧可能反映合理的主观视角。理解标注者视角与其社会人口属性之间的关系日益受到关注。以往工作多聚焦单一或有限组合的人口因素，但在真实场景中，标注视角受复杂社会背景影响，更细粒度的社会人口特征能更好解释观点差异。本文提出社会对比学习（Socio-Contrastive Learning），在联合建模标注者视角的同时学习社会人口表征。该方法有效融合社会人口特征与文本表示，预测标注视角表现优于标准拼接方法。所学表征还支持对人口因素与视角变异关系的分析与可视化。代码已开源：https://github.com/Leixin-Zhang/Socio_Contrastive_Learning
原文摘要 · Abstract (English)
Humans often hold different perspectives on the same issues. In many NLP tasks, annotation disagreement can reflect valid subjective perspectives. Modeling annotator perspectives and understanding their relationship with other human factors, such as socio-demographic attributes, have received increasing attention. Prior work typically focuses on single demographic factors or limited combinations. However, in real-world settings, annotator perspectives are shaped by complex social contexts, and finer-grained socio-demographic attributes can better explain human perspectives. In this work, we propose Socio-Contrastive Learning, a method that jointly models annotator perspectives while learning socio-demographic representations. Our method provides an effective approach for the fusion of socio-demographic features and textual representations to predict annotator perspectives, outperforming standard concatenation-based methods. The learned representations further enable analysis and visualization of how demographic factors relate to variation in annotator perspectives. Our code is available at GitHub: https://github.com/Leixin-Zhang/Socio_Contrastive_Learning
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