Robustness Gym: Unifying the NLP Evaluation Landscape

• URL: http://arxiv.org/abs/2101.04840v1
• Date: Wed, 13 Jan 2021 02:37:54 GMT
• Title: Robustness Gym: Unifying the NLP Evaluation Landscape
• Authors: Karan Goel, Nazneen Rajani, Jesse Vig, Samson Tan, Jason Wu, Stephan Zheng, Caiming Xiong, Mohit Bansal, Christopher R\'e
• Abstract summary: Deep neural networks are often brittle when deployed in real-world systems. Recent research has focused on testing the robustness of such models. We propose a solution in the form of Robustness Gym, a simple and evaluation toolkit.
• Score: 91.80175115162218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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), a simple and extensible evaluation toolkit that unifies 4 standard evaluation paradigms: subpopulations, transformations, evaluation sets, and adversarial attacks. By providing a common platform for evaluation, Robustness Gym enables practitioners to compare results from all 4 evaluation paradigms with just a few clicks, and to easily develop and share novel evaluation methods using a built-in set of abstractions. To validate Robustness Gym's utility to practitioners, we conducted a real-world case study with a sentiment-modeling team, revealing performance degradations of 18%+. To verify that Robustness Gym can aid novel research analyses, we perform the first study of state-of-the-art commercial and academic named entity linking (NEL) systems, as well as a fine-grained analysis of state-of-the-art summarization models. For NEL, commercial systems struggle to link rare entities and lag their academic counterparts by 10%+, while state-of-the-art summarization models struggle on examples that require abstraction and distillation, degrading by 9%+. Robustness Gym can be found at https://robustnessgym.com/

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