The ability of language to perpetuate inequality is most evident when individuals refer to, or talk about, other individuals in their utterances. While current studies of bias in NLP rely mainly on identifying hate speech or bias towards a specific group, we believe we can reach a more subtle and nuanced understanding of the interaction between bias and language use by modeling the speaker, the text, and the target in the text. In this paper, we introduce a dataset of 3033 English tweets by US Congress members annotated for interpersonal emotion, and `found supervision' for interpersonal group membership labels. We find that negative emotions such as anger and disgust are used predominantly in out-group situations, and directed predominantly at leaders of opposite parties. While humans can perform better than chance at identifying interpersonal group membership given an utterance, neural models perform much better; furthermore, a shared encoding between interpersonal group membership and interpersonal perceived emotion enabled some performance gains in the latter. This work aims to re-align the study of bias in NLP away from specific instances of bias to one which encapsulates the relationship between speaker, text, target and social dynamics. Data and code for this paper are available at https://github.com/venkatasg/Interpersonal-Dynamics

本文提出了一种基于情绪的方法来预测人际群体关系（IGR），研究表明微妙的情感信号表明了不同的偏见，使用美国国会成员的推文数据集，构建了一个被标注为人际情感的数据集，证明神经模型在识别IGR上显著优于人类。我们还发现，IGR和人际感知情感之间的共享编码可以提高两项任务的性能。

关于人际交往的话语：建模普遍的群体偏见和情感