Quantifying Stereotypes in Language
Yang Liu
A stereotype is a generalized perception of a specific group of humans. It is
often potentially encoded in human language, which is more common in texts on
social issues. Previous works simply define a sentence as stereotypical and
anti-stereotypical. However, the stereotype of a sentence may require
fine-grained quantification. In this paper, to fill this gap, we quantify
stereotypes in language by annotating a dataset. We use the pre-trained
language models (PLMs) to learn this dataset to predict stereotypes of
sentences. Then, we discuss stereotypes about common social issues such as hate
speech, sexism, sentiments, and disadvantaged and advantaged groups. We
demonstrate the connections and differences between stereotypes and common
social issues, and all four studies validate the general findings of the
current studies. In addition, our work suggests that fine-grained stereotype
scores are a highly relevant and competitive dimension for research on social
issues.