Corpus ID: 168169824

Defending Against Neural Fake News

  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},
  • 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

    Figures, Tables, and Topics from this paper.

    Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News
    • 1
    • Highly Influenced
    • PDF
    MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
    • 2
    • PDF
    Viable Threat on News Reading: Generating Biased News Using Natural Language Models
    The Limitations of Stylometry for Detecting Machine-Generated Fake News
    • 8
    • Highly Influenced
    • PDF
    Reevaluating Adversarial Examples in Natural Language
    • 5
    • PDF
    Automatic Detection of Machine Generated Text: A Critical Survey
    Fully Automatic Journalism: We Need to Talk About Nonfake News Generation
    • 1
    • PDF
    Fake news detection using discourse segment structure analysis
    • 2
    How Decoding Strategies Affect the Verifiability of Generated Text
    • 10
    • PDF


    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