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.

Intern: 
Lourdes Crivelli
Superviseur universitaire: 
Laurent Charlin
Province: 
Quebec
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