Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group. While this has led to progress recognizing and mitigating negative bias, and having a clear notion of the targeted group is necessary, it is not always practical. In this work we extrapolate to a broader notion of bias, rooted in social science and psychology literature. We move towards predicting interpersonal group relationship (IGR) - modeling the relationship between the speaker and the target in an utterance - using fine-grained interpersonal emotions as an anchor. We build and release a dataset of English tweets by US Congress members annotated for interpersonal emotion -- the first of its kind, and 'found supervision' for IGR labels; our analyses show that subtle emotional signals are indicative of different biases. While humans can perform better than chance at identifying IGR given an utterance, we show that neural models perform much better; furthermore, a shared encoding between IGR and interpersonal perceived emotion enabled performance gains in both tasks. Data and code for this paper are available at https://github.com/venkatasg/interpersonal-bias

翻译：当前自然语言处理中的偏见研究主要依赖于识别针对特定人口群体的（非期望或负面）偏见。虽然这促进了识别和缓解负面偏见的进展，且明确目标群体是必要的，但这并非总是可行的。在本研究中，我们基于社会科学和心理学文献，将偏见概念拓展至更广泛的范畴。我们转向预测人际群体关系——即建模话语中说话者与目标之间的关系——以细粒度人际情感为锚点。我们构建并发布了首个由美国国会议员发布的英语推文数据集，标注了人际情感，并为群体关系标签提供了“弱监督”；分析表明，微妙的情感信号能够指示不同类型的偏见。尽管人类在识别给定话语的群体关系时表现优于随机水平，但我们证明神经模型的性能更为优越；此外，群体关系与感知人际情感之间的共享编码促进了两项任务的性能提升。本文的数据与代码可在https://github.com/venkatasg/interpersonal-bias 获取。