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.

翻译：情绪分析中的歧义既源于潜在信息缺失，也源于文本解读的主观性。后者已得到广泛关注，但能否通过补充缺失信息来消除歧义？针对这一问题，我们提出了一种方法，可自动为歧义分类实例生成合理的上下文情境。这些生成的情境可作为不同读者解读的示例，因其能借助个体世界知识填补缺失信息。生成合理叙事这一任务颇具挑战：我们融合了短篇故事生成技术，以确保叙事的连贯性。由此构建的英文情感背景故事数据集EBS，首次实现了对情境化情绪分析的系统性全面考察。通过自动标注与人工标注，我们发现生成的叙事语境确实能明确特定情绪的解读——尤其对"宽慰"与"悲伤"两种情绪效果显著，而"喜悦"则无需额外语境辅助。