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：情感语料库通常基于关键词或话题标签搜索采样，或要求研究参与者生成文本实例。无论采用何种方法，这些语料库并非代表整个领域的均匀样本。我们假设这种数据获取方式会导致语料库中过度呈现的主题之间产生不真实的关联，从而损害模型的泛化能力。此类主题偏差可能导致对诸如“我组织了姨妈的葬礼服务”这类实例产生错误预测——当葬礼事件在标注为悲伤的实例中过度呈现时，尽管骄傲情绪在此情境中更为恰当。本文从数据与建模双重视角研究此类主题偏差。首先通过主题建模自动标注一组情感语料库，证明情感实际上与特定主题相关。进一步发现情感分类器会受到此类主题的混淆。最后论证，通过梯度反转实现对抗修正这一既定去偏方法可缓解该问题。本研究指出了现有情感语料库的不足，并表明需要更具代表性的资源才能对预测文本情感概念的模型进行公平评估。