Catherine Yeo


2020

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Defining and Evaluating Fair Natural Language Generation
Catherine Yeo | Alyssa Chen
Proceedings of the Fourth Widening Natural Language Processing Workshop

Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion. In this paper, we introduce a mathematical 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.
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