让AI理解语言背后的社会视角差异，基于2.8万条标注数据建模不同群体的解读变化。
Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings
- 融合文本与人口统计特征构建联合嵌入，捕捉社会视角多样性
- 相比纯文本模型，宏平均PR-AUC提升5.9%-6.5%且显著优于基线
- 验证了人口统计信息具有真实预测能力，非虚假关联
语言中的社会意义本质上具有视角性，随标注者背景、人口统计特征和意识形态而异。然而，大多数NLP系统将这种差异压缩为单一真值标签，忽略了多元解释。本文在包含2.8万条人工标注的数据集上，沿视角光谱建模社会维度，评估零样本、少样本与微调等多种范式，提出融合嵌入方法，整合文本与人口统计表征。所有融合策略均显著优于仅文本基线（相对宏平均PR-AUC提升5.9%-6.5%），随机打乱实验确认人口统计特征携带真实预测信号而非虚假相关。
原文摘要 · Abstract (English)
Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP systems collapse this variation into a single ground-truth label, ignoring the diversity of interpretations. In this work, we model social dimensions along a perspectivist spectrum, capturing how interpretations vary across demographic groups on a dataset consisting of 28k human annotations. We benchmark multiple modeling paradigms, including zero-shot, few-shot, and fine-tuned approaches, and propose fusion embeddings that integrate textual and demographic representations. Our fusion models yield consistent and statistically significant improvements over text-only baselines across all fusion strategies (+5.9-6.5% relative macro PR-AUC), with shuffle ablations confirming that demographic profiles carry genuine predictive signal rather than spurious correlations.
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