Deep Learning to assist requirement translation

Cutting edge techniques in artificial intelligence will be applied to extract semantic information from natural language and work towards building a system that can help engineers write clearer and less ambiguous requirements for complex systems. Models will be developed that are similar to current techniques used by popular translation tools, and will be adapted for paraphrasing and ambiguity reduction.

Faculty Supervisor:

Thomas Trappenberg

Student:

Michael Traynor

Partner:

QRA Corp

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

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