He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation

Abstract

Social biases with respect to demographics (e.g., gender, age, race) in datasets are often encoded in the large pre-trained language models trained on them. Prior works have largely focused on mitigating biases in context-free representations, with recent shift to contextual ones. While this is useful for several word and sentence-level classification tasks, mitigating biases in only the representations may not suffice to use these models for language generation tasks, such as auto-completion, summarization, or dialogue generation. In this paper, we propose an approach to mitigate social biases in BERT, a large pre-trained contextual language model, and show its effectiveness in fill-in-the-blank sentence completion and summarization tasks. In addition to mitigating biases in BERT, which in general acts as an encoder, we propose lexical co-occurrence-based bias penalization in the decoder units in generation frameworks, and show bias mitigation in summarization. Finally, our approach results in better debiasing of BERT-based representations compared to post training bias mitigation, thus illustrating the efficacy of our approach to not just mitigate biases in representations, but also generate text with reduced biases.