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
Research Topics
Deep Learning
Medical Machine Learning
Natural Language Processing
Reasoning

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

Collaborating Alumni - McGill University
Master's Research - McGill University
Collaborating researcher
Collaborating researcher
PhD - McGill University
PhD - McGill University
PhD - McGill University
Principal supervisor :
Master's Research - McGill University
Research Intern - McGill University
PhD - McGill University
Co-supervisor :
PhD - McGill University
Co-supervisor :
Postdoctorate - McGill University
Master's Research - McGill University
Master's Research - McGill University
Research Intern - McGill University University
Research Intern - McGill University
PhD - McGill University
Principal supervisor :
PhD - McGill University
PhD - McGill University
PhD - McGill University
Undergraduate - McGill University
PhD - McGill University
Research Intern - McGill University University
Master's Research - McGill University

Publications

A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques
Malik H. Altakrori
Thomas Scialom
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification
Yu Lu Liu
Rachel Bawden
Thomas Scaliom
Benoı̂t Sagot
Characterizing Idioms: Conventionality and Contingency
Michaela Socolof
Michael Wagner
Idioms are unlike most phrases in two important ways. First, words in an idiom have non-canonical meanings. Second, the non-canonical meanin… (see more)gs of words in an idiom are contingent on the presence of other words in the idiom. Linguistic theories differ on whether these properties depend on one another, as well as whether special theoretical machinery is needed to accommodate idioms. We define two measures that correspond to the properties above, and we show that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. Our results suggest that introducing special machinery to handle idioms may not be warranted.
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
Meng Cao
Yue Dong
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Zichao Li
Prakhar Sharma
Xing Han Lu
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Zichao Li
Prakhar Sharma
Xing Han Lu
Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment.In this paper, we a… (see more)sk the question: Can we improve QA systems further post-deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system’s performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer.We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers.The feedback contains both structured ratings and unstructured natural language explanations.We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers.
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Kushal Arora
Layla El Asri
Hareesh Bahuleyan
Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis for … (see more)this brittleness of generation models is that it is caused by the training and the generation procedure mismatch, also referred to as exposure bias. In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective. We show that exposure bias leads to an accumulation of errors during generation, analyze why perplexity fails to capture this accumulation of errors, and empirically show that this accumulation results in poor generation quality.
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences
Jad Kabbara
Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of lan… (see more)guage. In this paper, we investigate the capabilities of transformer models to perform NLI on cases involving presupposition. First, we present simple heuristics to create alternative “contrastive” test cases based on the ImpPres dataset and investigate the model performance on those test cases. Second, to better understand how the model is making its predictions, we analyze samples from sub-datasets of ImpPres and examine model performance on them. Overall, our findings suggest that NLI-trained transformer models seem to be exploiting specific structural and lexical cues as opposed to performing some kind of pragmatic reasoning.
Learning with Rejection for Abstractive Text Summarization
Meng Cao
Yue Dong
Jingyi He
Question Personalization in an Intelligent Tutoring System
Sabina Elkins
Robert Belfer
Ekaterina Kochmar
Iulian V. Serban
Source-summary Entity Aggregation in Abstractive Summarization.
José-ángel González
Annie Priyadarshini Louis