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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