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（不受欢迎或负面）偏见。尽管这推动了识别和缓解负面偏见的进展，且明确目标群体概念具有必要性，但在实践中往往难以实现。本研究基于社会科学与心理学文献，将偏见概念扩展至更广义的范畴。我们致力于预测人际群体关系（IGR）——通过细粒度人际情绪作为锚点，建模话语中说话者与目标对象之间的关系。我们构建并发布了首个由美国国会议员推文组成的人际情绪标注数据集，并为IGR标签提供了"监督线索"；分析表明，微妙的情感信号能够指示不同的偏见。实验显示，人类在根据话语识别IGR时虽能优于随机水平，但神经模型的性能显著更优；此外，IGR与人际感知情绪的共享编码机制在两个任务中均带来了性能提升。本文数据与代码发布在 https://github.com/venkatasg/interpersonal-bias