Talk Title: Reasoning about Social Dynamics and Social Bias in Language
Talk Abstract: Humans easily make inferences to reason about the social and power dynamics of situations (e.g., stories about everyday interactions), but such reasoning is still a challenge for modern NLP systems. In this talk, I will address how we can make machines reason about social commonsense and social biases in text, and how this reasoning could be applied in downstream applications.
In the first part, I will discuss PowerTransformer, our new unsupervised model for controllable debiasing of text through the lens of connotation frames of power and agency. Trained using a combined reconstruction and paraphrasing objective, this model can rewrite story sentences such that its characters are portrayed with more agency and decisiveness. After establishing its performance through automatic and human evaluations, we show how PowerTransformer can be used to mitigate gender bias in portrayals of movie characters. Then, I will introduce Social Bias Frames, a conceptual formalism that models the pragmatic frames in which people project social biases and stereotypes onto others to reason about biased or harmful implications in language. Using a new corpus of 150k structured annotations, we show that models can learn to reason about high-level offensiveness of statements, but struggle to explain why a statement might be harmful. I will conclude with future directions for better reasoning about social dynamics and social biases.
Bio: Maarten Sap is a final year PhD student in the University of Washington’s natural language processing (NLP) group, advised by Noah Smith and Yejin Choi. His research focuses on endowing NLP systems with social intelligence and social commonsense, and understanding social inequality and bias in language. In the past, he’s interned at AI2 on project Mosaic working on social commonsense reasoning, and at Microsoft Research working on long-term memory and storytelling with Eric Horvitz.