Defining and Evaluating Fair Natural Language Generation
arXiv:2008.01548
Abstract
Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion. In this paper, we introduce a framework of fairness for NLG followed by an evaluation of gender biases in two state-of-the-art language models. Our analysis provides a theoretical formulation for biases in NLG and empirical evidence that existing language generation models embed gender bias.
7 pages, 2 figures, to be published in Proceedings of the The Fourth Widening Natural Language Processing Workshop at ACL