Portrait de Jackie Cheung

Jackie Cheung

Membre académique principal
Chaire en IA Canada-CIFAR
Directeur scientifique adjoint, Mila, Professeur agrégé, McGill University, École d'informatique
Chercheur consultant, Microsoft Research

Biographie

Je suis professeur agrégé à l'École d’informatique de l’Université McGill et chercheur consultant à Microsoft Research.

Mon groupe mène des recherches sur le traitement du langage naturel (NLP), un domaine de l'intelligence artificielle qui implique la construction de modèles informatiques de langages humains tels que l'anglais ou le français. Le but de nos recherches est de développer des méthodes informatiques de compréhension du texte et de la parole, afin de générer un langage fluide et adapté au contexte.

Dans notre laboratoire, nous étudions des techniques statistiques d’apprentissage automatique pour analyser et faire des prédictions sur la langue. Plusieurs projets en cours incluent la synthèse de fiction, l'extraction d'événements à partir d’un texte et l'adaptation de la langue à différents genres.

Étudiants actuels

Doctorat - McGill University
Doctorat - McGill University
Stagiaire de recherche - McGill University
Maîtrise recherche - McGill University
Co-superviseur⋅e :
Doctorat - McGill University
Maîtrise professionnelle - McGill University
Maîtrise recherche - McGill University
Doctorat - McGill University
Co-superviseur⋅e :
Maîtrise recherche - McGill University
Doctorat - McGill University
Co-superviseur⋅e :
Stagiaire de recherche - McGill University University
Postdoctorat - McGill University
Stagiaire de recherche - McGill University
Stagiaire de recherche - McGill University
Stagiaire de recherche - McGill University
Maîtrise recherche - McGill University
Doctorat - McGill University
Superviseur⋅e principal⋅e :
Stagiaire de recherche - 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… (voir plus)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, … (voir plus)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