Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions. (arXiv:2201.00768v1 [cs.CL])

Recent natural language processing (NLP) techniques have accomplished high
performance on benchmark datasets, primarily due to the significant improvement
in the performance of deep learning. The advances in the research community
have led to great enhancements in state-of-the-art production systems for NLP
tasks, such as virtual assistants, speech recognition, and sentiment analysis.
However, such NLP systems still often fail when tested with adversarial
attacks. The initial lack of robustness exposed troubling gaps in current
models' language understanding capabilities, creating problems …

challenges language