Measure and Improve Robustness in NLP Models: A Survey. (arXiv:2112.08313v2 [cs.CL] UPDATED)

Web: http://arxiv.org/abs/2112.08313

As NLP models achieved state-of-the-art performances over benchmarks and
gained wide applications, it has been increasingly important to ensure the safe
deployment of these models in the real world, e.g., making sure the models are
robust against unseen or challenging scenarios. Despite robustness being an
increasingly studied topic, it has been separately explored in applications
like vision and NLP, with various definitions, evaluation and mitigation
strategies in multiple lines of research. In this paper, we aim to provide a
unifying …

arxiv models nlp robustness survey