用偏好学习只标注情绪波动点，大幅降低情感标注负担。
PREFAB: PREFerence-based Affective Modeling for Low-Budget Self-Annotation
- 只标记情绪变化关键段，其余由模型插值补全
- 用户标注量减少50%以上，仍保持高准确率
- 适合资源有限但需精细情感数据的研究者
自标注是情感计算中获取情感状态标签的金标准。现有方法通常依赖全程标注，要求用户在整段会话中持续标记情感状态，虽能获得细粒度数据，但耗时费力，易产生疲劳与误差。为解决此问题，我们提出PREFAB，一种低成本的回溯式自标注方法，聚焦于情感波动区域而非全程标注。基于峰终定律与情感的序数表征，PREFAB采用偏好学习模型检测相对情感变化，引导标注者仅对选定片段进行标注，其余部分由模型插值完成。我们还引入预览机制，提供简短上下文提示辅助标注。通过技术性能研究与25名参与者用户研究验证，结果表明，PREFAB在建模情感波动方面优于基线方法，同时显著降低工作量（并有条件减轻时间负担）。重要的是，其提升了标注者信心，且未降低标注质量。
原文摘要 · Abstract (English)
Self-annotation is the gold standard for collecting affective state labels in affective computing. Existing methods typically rely on full annotation, requiring users to continuously label affective states across entire sessions. While this process yields fine-grained data, it is time-consuming, cognitively demanding, and prone to fatigue and errors. To address these issues, we present PREFAB, a low-budget retrospective self-annotation method that targets affective inflection regions rather than full annotation. Grounded in the peak-end rule and ordinal representations of emotion, PREFAB employs a preference-learning model to detect relative affective changes, directing annotators to label only selected segments while interpolating the remainder of the stimulus. We further introduce a preview mechanism that provides brief contextual cues to assist annotation. We evaluate PREFAB through a technical performance study and a 25-participant user study. Results show that PREFAB outperforms baselines in modeling affective inflections while mitigating workload (and conditionally mitigating temporal burden). Importantly PREFAB improves annotator confidence without degrading annotation quality.
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