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Various robustness evaluation methodologies from different perspectives havebeen proposed for different natural language processing (NLP) tasks. Thesemethods have often focused on either universal or task-specific generalizationcapabilities. In this work, we propose a multilingual robustness evaluationplatform for NLP tasks (TextFlint) that incorporates universal texttransformation, task-specific transformation, adversarial attack,subpopulation, and their combinations to provide comprehensive robustnessanalysis. TextFlint enables practitioners to automatically evaluate theirmodels from all aspects or to customize their evaluations as desired with justa few lines of code. To guarantee user acceptability, all the texttransformations are linguistically based, and we provide a human evaluation foreach one. TextFlint generates complete analytical reports as well as targetedaugmented data to address the shortcomings of the model's robustness. Tovalidate TextFlint's utility, we performed large-scale empirical evaluations(over 67,000 evaluations) on state-of-the-art deep learning models, classicsupervised methods, and real-world systems. Almost all models showedsignificant performance degradation, including a decline of more than 50% ofBERT's prediction accuracy on tasks such as aspect-level sentimentclassification, named entity recognition, and natural language inference.Therefore, we call for the robustness to be included in the model evaluation,so as to promote the healthy development of NLP technology.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing