Corpus ID: 231592355

Robustness Gym: Unifying the NLP Evaluation Landscape

@article{Goel2021RobustnessGU,
  title={Robustness Gym: Unifying the NLP Evaluation Landscape},
  author={Karan Goel and Nazneen Rajani and J. Vig and Samson Tan and J. Wu and Stephan Zheng and Caiming Xiong and M. Bansal and Christopher R'e},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.04840}
}

• Karan Goel, Nazneen Rajani, +6 authors Christopher R'e
• Published 2021
• Computer Science
• ArXiv

Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems. Consequently, recent research has focused on testing the robustness of such models, resulting in a diverse set of evaluation methodologies ranging from adversarial attacks to rule-based data transformations. In this work, we identify challenges with evaluating NLP systems and propose a solution in the form of Robustness Gym (RG),1 a simple and extensible evaluation… Expand

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