Quantifying Stereotypes in Language
This work addresses the need for fine-grained stereotype quantification in NLP, which is incremental as it builds on prior binary classifications to enhance research on social issues.
The paper tackles the problem of quantifying stereotypes in language by creating an annotated dataset and using pre-trained language models to predict fine-grained stereotype scores for sentences, demonstrating connections to social issues like hate speech and sexism.
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.