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Women are often perceived as junior to their male counterparts, even withinthe same job titles. While there has been significant progress in theevaluation of gender bias in natural language processing (NLP), existingstudies seldom investigate how biases toward gender groups change whencompounded with other societal biases. In this work, we investigate howseniority impacts the degree of gender bias exhibited in pretrained neuralgeneration models by introducing a novel framework for probing compound bias.We contribute a benchmark robustness-testing dataset spanning two domains, U.S.senatorship and professorship, created using a distant-supervision method. Ourdataset includes human-written text with underlying ground truth and pairedcounterfactuals. We then examine GPT-2 perplexity and the frequency of genderedlanguage in generated text. Our results show that GPT-2 amplifies bias byconsidering women as junior and men as senior more often than the ground truthin both domains. These results suggest that NLP applications built using GPT-2may harm women in professional capacities.

Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation