Warning: this paper contains content that may be offensive or upsetting.
Language has the power to reinforce stereotypes and project social biases
onto others. At the core of the challenge is that it is rarely what is stated
explicitly, but rather the implied meanings, that frame people's judgments
about others. For example, given a statement that "we shouldn't lower our
standards to hire more women," most listeners will infer the implicature
intended by the speaker -- that "women (candidates) are less qualified." Most
semantic formalisms, to date, do not capture such pragmatic implications in
which people express social biases and power differentials in language.
We introduce Social Bias Frames, a new conceptual formalism that aims to
model the pragmatic frames in which people project social biases and
stereotypes onto others. In addition, we introduce the Social Bias Inference
Corpus to support large-scale modelling and evaluation with 150k structured
annotations of social media posts, covering over 34k implications about a
thousand demographic groups.
We then establish baseline approaches that learn to recover Social Bias
Frames from unstructured text. We find that while state-of-the-art neural
models are effective at high-level categorization of whether a given statement
projects unwanted social bias (80% F1), they are not effective at spelling out
more detailed explanations in terms of Social Bias Frames. Our study motivates
future work that combines structured pragmatic inference with commonsense
reasoning on social implications.

本研究提出了一种名为 “社会偏见框架” 的新的概念形式化模型，以模拟人们如何将社会偏见和刻板印象投射到其他人身上的框架，并建立了一个名为 “社会偏见推理语料库” 的数据集来支持大规模建模和评估，分析表明当前最先进的神经模型虽然能够高效地对是否含有不良社会偏见进行分类，但在详细解释社会偏见框架方面并不有效。本研究为今后的研究工作提供了指导，即将结构化的语用推理与社会影响的通识推理相结合。