@inproceedings{yeo-chen-2020-defining,
    title = "Defining and Evaluating Fair Natural Language Generation",
    author = "Yeo, Catherine  and
      Chen, Alyssa",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.winlp-1.27/",
    doi = "10.18653/v1/2020.winlp-1.27",
    pages = "107--109",
    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 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."
}