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Improving Conditional Text Generation Algorithms
The aim of this project is to survey and develop methods for improving text generation algorithms. Natural Language Generation (NLG) is a subfield of Deep Learning that studies how to enable computers to write coherent texts. Current methods lack the capacity to adapt the content they generate, producing articles that are not always coherent nor factually correct. We will study ways of conditioning the algorithms to create more relevant content that adapts to the needs of the writer. The internship will be done in collaboration with MarketMuse, an AI company that specializes in content creation and is looking for new ways to improve their algorithms.
Faculty Supervisor:
Laurent Charlin
Student:
Partner:
Marketmuse Laboratoire Canada Inc.
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
HEC Montréal
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.