Robustness Gym: Unifying the NLP Evaluation Landscape
Karan Goel, Nazneen Fatema Rajani, Jesse Vig, Zachary Taschdjian, Mohit Bansal, Christopher Ré
Abstract
Despite impressive performance on standard benchmarks, natural language processing (NLP) models are often brittle when deployed in real-world systems. 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, RG enables practitioners to compare results from disparate evaluation paradigms with a single click, and to easily develop and share novel evaluation methods using a built-in set of abstractions. RG is under active development and we welcome feedback & contributions from the community.
BibTeX
@inproceedings{goel-etal-2021-robustness,
title = "Robustness Gym: Unifying the {NLP} Evaluation Landscape",
author = "Goel, Karan and
Rajani, Nazneen Fatema and
Vig, Jesse and
Taschdjian, Zachary and
Bansal, Mohit and
R{\'e}, Christopher",
editor = "Sil, Avi and
Lin, Xi Victoria",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-demos.6/",
doi = "10.18653/v1/2021.naacl-demos.6",
pages = "42--55"
}