论文标题
定义和评估公平的自然语言生成
Defining and Evaluating Fair Natural Language Generation
论文作者
论文摘要
我们的工作着重于句子完成的自然语言生成（NLG）任务中出现的偏见。在本文中，我们为NLG介绍了一个公平性的框架，然后在两种最先进的语言模型中评估性别偏见。我们的分析为NLG和经验证据中现有语言生成模型嵌入性别偏见提供了理论表述。
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.
