Corpus ID: 168169824

Defending Against Neural Fake News

@article{Zellers2019DefendingAN,
  title={Defending Against Neural Fake News},
  author={Rowan Zellers and Ari Holtzman and Hannah Rashkin and Yonatan Bisk and Ali Farhadi and F. Roesner and Yejin Choi},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.12616}
}
  • Rowan Zellers, Ari Holtzman, +4 authors Yejin Choi
  • Published 2019
  • Computer Science
  • ArXiv
  • Recent progress in natural language generation has raised dual-use concerns. [...] Key Result We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.Expand Abstract
    160 Citations

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    References

    SHOWING 1-10 OF 55 REFERENCES
    Language GANs Falling Short
    • 74
    • PDF
    The Curious Case of Neural Text Degeneration
    • 251
    • PDF
    Unifying Human and Statistical Evaluation for Natural Language Generation
    • 58
    • PDF
    Toward Controlled Generation of Text
    • 475
    • PDF