让模型同时理解多个标注者的观点，提升立场识别准确率。
Multi-Perspective Stance Detection
- 引入多视角标注数据，构建感知视角的分类模型
- 多视角方法比单标签基线提升分类性能
- 适合关注AI公平性与可解释性的研究者
主观性自然语言处理任务通常依赖多位标注者的人工标注，其判断可能因背景和人生经历差异而不同。传统方法常将多份标注合并为单一真实标签，忽略了标注者分歧带来的视角多样性。本初步研究探讨在分类任务中保留多份标注对模型准确率的影响。方法上，研究了面向视角的立场检测模型性能，并进一步检验标注者分歧是否影响模型置信度。结果表明，多视角方法优于使用单一标签的基线模型。这说明设计更具包容性的视角感知型AI不仅是实现负责任、伦理化AI的关键第一步，还能获得比传统方法更优的效果。
原文摘要 · Abstract (English)
Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences. Traditional methods often aggregate multiple annotations into a single ground truth, disregarding the diversity in perspectives that arises from annotator disagreement. In this preliminary study, we examine the effect of including multiple annotations on model accuracy in classification. Our methodology investigates the performance of perspective-aware classification models in stance detection task and further inspects if annotator disagreement affects the model confidence. The results show that multi-perspective approach yields better classification performance outperforming the baseline which uses the single label. This entails that designing more inclusive perspective-aware AI models is not only an essential first step in implementing responsible and ethical AI, but it can also achieve superior results than using the traditional approaches.
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