Natural language processing (NLP) aims to enable computers to use human languages – so that people can, for example, interact with computers naturally; or communicate with people who don’t speak a common language; or manipulate speech or text data at scales not otherwise possible. The NLP group at George Mason Computer Science is interested in all aspects of NLP, with a focus on building tools for under-served languages.
We are currently working on multilingual models, on building Machine Translation robust to L2-language variations, and on NLP for documentation of endangered languages.
Our research is/has been supported by the following organizations/companies:
Most languages of the world are “oral”: they are not traditionally written and even if an alphabet exists, the community doesn’t usually use it. Hence, building NLP systems that can directly operate on speech input is paramount.
Human language is marked by considerable diversity around the world, and the surface form of languages varies substantially. Morphology describes the way through which different word forms arise from lexemes. Computational morphology attempts to reproduce this process across languages, or uses machine learning models to model/discover the morphophonological processes that exist in a language.
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).
Language Documentation aims at producing a permanent record that describes a language as used by its language community by producing a formal grammatical description along with a lexicon. Our group works on integrating NLP systems into the documentation workflow, aiming to speed-up the process and help the work of field linguists and language communities.
Machine Translation is the task of translating between human languages using computers. Starting from simple word-for-word rule-based system in 1950s, we now have large multilingual neural models that can learn translate between dozens of languages.
An exciting research direction that we pursue at GMU NLP is building multi-lingual and polyglot systems. The languages of the world often share similar characteristics, and training systems cross-lingually allows us to leverage these similarities and overcome data scarcity issues.
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