用合理叙事补全情绪描述缺失信息，提升解读准确性
Disambiguation of Emotion Annotations by Contextualizing Events in Plausible Narratives
- 通过生成合理故事背景补全情绪文本的缺失信息
- 生成上下文使悲伤和释然情绪解读准确率显著提升
- 适合需要提升情绪分析鲁棒性的自然语言处理研究者
情绪分析中的歧义既源于信息缺失，也源于主观解读。现有研究多关注后者，但能否通过补全信息来解决歧义？我们提出一种自动生成合理情境的方法，为原本模糊的情绪分类实例提供可能的解释背景。这些生成的情境可反映不同读者基于自身常识对文本的理解差异。该任务具有挑战性：我们结合短篇故事生成技术，构建了首个系统性的英文情绪背景数据集EBS（Emotional BackStories）。通过自动与人工标注发现，生成的上下文确实能澄清特定情绪的解读，尤其对‘悲伤’和‘释然’有显著帮助，而‘喜悦’则无需额外上下文。
原文摘要 · Abstract (English)
Ambiguity in emotion analysis stems both from potentially missing information and the subjectivity of interpreting a text. The latter did receive substantial attention, but can we fill missing information to resolve ambiguity? We address this question by developing a method to automatically generate reasonable contexts for an otherwise ambiguous classification instance. These generated contexts may act as illustrations of potential interpretations by different readers, as they can fill missing information with their individual world knowledge. This task to generate plausible narratives is a challenging one: We combine techniques from short story generation to achieve coherent narratives. The resulting English dataset of Emotional BackStories, EBS, allows for the first comprehensive and systematic examination of contextualized emotion analysis. We conduct automatic and human annotation and find that the generated contextual narratives do indeed clarify the interpretation of specific emotions. Particularly relief and sadness benefit from our approach, while joy does not require the additional context we provide.
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