Bias Score: Estimating Gender Bias in Sentence Representations

The ever-increasing number of applications based on semantic text analysis is making natural language understanding a fundamental task. Language models are used for a variety of tasks, such as parsing CVs or improving web search results. At the same time, concern is growing around embedding-based language models, which often exhibit social bias and lack of transparency, despite their popularity and widespread use. Word embeddings in particular exhibit a large amount of gender bias, and they have been shown to reflect social stereotypes. Recently, sentence embeddings have been introduced as a novel and powerful technique to represent entire sentences as vectors. However, traditional methods for estimating gender bias cannot be applied to sentence representations, because gender-neutral entities cannot be easily identified and listed. We propose a new metric to estimate gender bias in sentence embeddings, named bias score. Our solution, leveraging the semantic importance of individual words and previous research on gender bias in word embeddings, is able to discern between correct and biased gender information at sentence level. Experiments on a real-world dataset demonstrates that our novel metric identifies gender stereotyped sentences