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Developing an Intelligent Conversational Agent Architecture related to the Banking Domain
This research project aims at creating a robust, efficient and reliable conversational agent for the banking domain that will offer a high level of performance in both key areas of conversational agent architecture: Natural Language Understanding and Response Generation. Natural language understanding approaches, retrieval-based models, as well as deep learning will be used to develop the architecture of the conversational agent in this specialized domain.
Faculty Supervisor:
Fatiha Sadat
Student:
Schahrazed Fennouh
Partner:
Banque Nationale du Canada
Discipline:
Computer science
Sector:
Information and communications technologies
University:
Université du Québec à Montréal
Program:
Accelerate
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