Our research encompasses all aspects of NLP, from modeling basic linguistic phenomena to designing practical text processing systems, and developing new machine learning methods.

A central theme of our research is developing creative new algorithms for processing text and other information (images, software, chemical reaction) to solve important social problems and expand the capabilities of the field. We are particularly interested in pushing the NLP boundaries to new tasks. We were the first to demonstrate statistical decipherment of a dead language, develop an automatic Civilization player that learns from text manuals, and design a system for generating Wikipedia articles. Current research includes designing NLP systems capable of performing competitively without requiring expensive and slow to obtain manual annotations. Instead, we are exploring alternative learning approaches which can benefit from other sources of supervision. Examples include grounding language in control applications, cross-lingual transfer, and learning with declarative rules.

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