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).

Md Mahfuz Ibn Alam
Md Mahfuz Ibn Alam
PhD Student

I work on robustness

Antonios Anastasopoulos
Antonios Anastasopoulos
Assistant Professor

I work on multilingual models, machine translation, speech recognition, and NLP for under-served languages.

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