理解和对抗刻板印象：刻板印象内容模型的计算方法
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model
摘要：
刻板印象语言表达了人们对不同社会类别的普遍看法。许多刻板印象是公开的消极，而其他一些刻板印象表面上可能是积极的，但仍然会导致消极的后果。在这项工作中，我们提出了一种计算方法来解释刻板印象的文本通过刻板印象内容模型（SCM），一个全面的因果关系理论，从社会心理学。SCM提出，刻板印象可以沿着两个主要维度来理解：温暖和能力。我们提出了一种方法定义的温暖和能力轴的语义嵌入空间，并表明，由这个子空间定义的四个象限准确地表示温暖和能力的概念，根据注释的词典。然后，我们将我们的计算SCM模型的文本刻板印象的数据，并表明它相比，在心理学文献中的调查为基础的研究。此外，我们还探讨了各种策略，以反陈规定型观念来对抗陈规定型观念。众所周知，用反定型观念的例子来反对定型观念是减少偏见思维的最有效方法之一，但以前没有研究过产生反定型观念的问题。因此，更好地了解如何产生现实和有效的反陈规定型观念，有助于解决陈规定型观念、偏见和歧视等紧迫的社会问题。
英文摘要：
Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM model to textual stereotype data and show that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to counter stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the most effective ways to reduce biased thinking, yet the problem of generating anti-stereotypes has not been previously studied. Thus, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination.
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