Robustness
NLP systems are typically trained and evaluated in “clean” settings, over data without significant noise. However, systems deployed in the real world need to deal with vast amounts of noise. At GMU NLP we work towards making NLP systems more robust to several types of noise (adversarial or naturally occuring).