Portrait of Jackie Cheung

Jackie Cheung

Core Academic Member
Canada CIFAR AI Chair
Associate Scientific Director, Mila, Associate Professor, McGill University, School of Computer Science
Consultant Researcher, Microsoft Research

Biography

I am an associate professor in the School of Computer Science at McGill University and a consultant researcher at Microsoft Research.

My group investigates natural language processing, an area of AI research that builds computational models of human languages, such as English or French. The goal of our research is to develop computational methods for understanding text and speech in order to generate language that is fluent and context appropriate.

In our lab, we investigate statistical machine learning techniques for analyzing and making predictions about language. Some of my current projects focus on summarizing fiction, extracting events from text, and adapting language across genres.

Current Students

PhD - McGill University
PhD - McGill University
Research Intern - McGill University
Master's Research - McGill University
Co-supervisor :
PhD - McGill University
Professional Master's - McGill University
Postdoctorate - McGill University
Master's Research - McGill University
Master's Research - McGill University
Research Intern - McGill University University
Postdoctorate - McGill University
Research Intern - McGill University
Research Intern - McGill University
Research Intern - McGill University
Master's Research - McGill University
PhD - McGill University
Principal supervisor :
Research Intern - McGill University

Publications

Predicting Success in Goal-Driven Human-Human Dialogues
Michael Noseworthy
In goal-driven dialogue systems, success is often defined based on a structured definition of the goal. This requires that the dialogue syst… (see more)em be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal. However, in many human-human dialogues the diversity of goals makes it infeasible to define success in such a way. To address this scenario, we consider the task of automatically predicting success in goal-driven human-human dialogues using only the information communicated between participants in the form of text. We build a dataset from stackoverflow.com which consists of exchanges between two users in the technical domain where ground-truth success labels are available. We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition. We show this model outperforms rule-based heuristics and other baselines as it is able to detect patterns over the course of a dialogue and capture notions such as gratitude.
Detecting Large Concept Extensions for Conceptual Analysis
L. Chartrand
Mohamed Bouguessa
Nifty Assignments
Nick Parlante
Julie Zelenski
Dave Feinberg
Kunal Mishra
Josh Hug
Kevin Wayne
Michael Guerzhoy
François Pitt
I suspect that students learn more from our programming assignments than from our much sweated-over lectures, with their slide transitions, … (see more)clip art, and joke attempts. A great assignment is deliberate about where the student hours go, concentrating the student's attention on material that is interesting and useful. The best assignments solve a problem that is topical and entertaining, providing motivation for the whole stack of work. Unfortunately, creating great programming assignments is both time consuming and error prone. The Nifty Assignments special session is all about promoting and sharing the ideas and ready-to-use materials of successful assignments.
Computer-Assisted Conceptual Analysis of Textual Data as Applied to Philosophical Corpuses
Jean Guy Meunier
L. Chartrand
Mathieu Valette
Marie-noëlle Bayle