Emotion corpora are typically sampled based on keyword/hashtag search or by asking study participants to generate textual instances. In any case, these corpora are not uniform samples representing the entirety of a domain. We hypothesize that this practice of data acquisition leads to unrealistic correlations between overrepresented topics in these corpora that harm the generalizability of models. Such topic bias could lead to wrong predictions for instances like "I organized the service for my aunt's funeral." when funeral events are over-represented for instances labeled with sadness, despite the emotion of pride being more appropriate here. In this paper, we study this topic bias both from the data and the modeling perspective. We first label a set of emotion corpora automatically via topic modeling and show that emotions in fact correlate with specific topics. Further, we see that emotion classifiers are confounded by such topics. Finally, we show that the established debiasing method of adversarial correction via gradient reversal mitigates the issue. Our work points out issues with existing emotion corpora and that more representative resources are required for fair evaluation of models predicting affective concepts from text.

翻译：情感语料通常基于关键词/话题标签搜索或通过要求研究参与者生成文本实例来采样。无论如何，这些语料并非代表领域整体的统一样本。我们假设这种数据采集方法会导致语料中过度呈现的主题之间产生不切实际的关联，从而损害模型的泛化能力。这种主题偏差可能导致对诸如"我为我姑姑的葬礼组织了服务"等实例产生错误预测——当悲伤标签对应的实例中"葬礼"事件被过度呈现时，即使此处更适用"自豪"情感。本文从数据与建模两个角度研究这种主题偏差。首先通过主题建模自动标注一组情感语料，发现情感实际上与特定主题相关；进而观察到情感分类器会受这些主题的混淆；最终证明通过梯度反转进行对抗修正的既定去偏方法可缓解此问题。本研究揭示了现有情感语料存在的问题，并提出需要更具代表性的资源来公平评估从文本预测情感概念的模型。