학술논문
Defining and Evaluating Fair Natural Language Generation
Document Type
Working Paper
Author
Source
Subject
Language
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.
Comment: 7 pages, 2 figures, to be published in Proceedings of the The Fourth Widening Natural Language Processing Workshop at ACL
Comment: 7 pages, 2 figures, to be published in Proceedings of the The Fourth Widening Natural Language Processing Workshop at ACL