UNQOVERing Stereotyping Biases via Underspecified Questions

Tao Li, Tushar Khot, Daniel Khashabi, Ashish Sabharwal and Vivek Srikumar

Findings of EMNLP, 2020.

Abstract

While language embeddings have been shownto have stereotyping biases, how these biases affect downstream question answering (QA)models remains unexplored. We present UN-QOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence. We design a formalism that isolates the aforementioned errors. As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion. We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models. Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size.

Links

Bib Entry 

@inproceedings{li2020unqovering,
  author = {Li, Tao and Khot, Tushar and Khashabi, Daniel and Sabharwal, Ashish and Srikumar, Vivek},
  title = {{UNQOVERing Stereotyping Biases via Underspecified Questions}},
  booktitle = {Findings of EMNLP},
  year = {2020}
}