Jackie Cheung

Google Scholar

Yu Bai

Stagiaire de recherche - McGill

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Superviseur⋅e principal⋅e :

Maîtrise recherche - McGill

Doctorat - McGill

Stagiaire de recherche - McGill

Maxime Darrin

Doctorat - McGill

Co-superviseur⋅e :

Maîtrise recherche - McGill

Doctorat - McGill

Aylin Erman

Doctorat - McGill

Co-superviseur⋅e :

Dan Poenaru

Ori Ernst

Postdoctorat - McGill

Maîtrise recherche - McGill

Google Scholar

Jules Gagnon-marchand

Maîtrise recherche - McGill

Sienna Hsu

Stagiaire de recherche - McGill University

Steven Koniaev

Stagiaire de recherche - McGill

Zichao Li

Doctorat - McGill

Superviseur⋅e principal⋅e :

Siva Reddy

Yu Lu Liu

Maîtrise recherche - McGill

Caleb Moses

Doctorat - McGill

Martin Pömsl

Maîtrise recherche - McGill

Google Scholar

Ian Porada

Doctorat - McGill

Doctorat - McGill

Baccalauréat - McGill

Cesare Spinoso-Di Piano

Doctorat - McGill

Nathan Zeweniuk

Stagiaire de recherche - McGill University

Xiyuan Zou

Stagiaire de recherche - McGill

Publications

A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge

Ali Emami

Adam Trischler

Kaheer Suleman

We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice … (voir plus)of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.

2018-06-01

North American Chapter of the Association for Computational Linguistics (publié)

Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization

Kian Kenyon-Dean

Doina Precup

We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation … (voir plus)learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.

2018-06-01

Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (publié)

arxiv.org

Advances in Artificial Intelligence

Ebrahim Bagheri

2018-01-01

Lecture Notes in Computer Science (publié)

Advances in Artificial Intelligence

Ebrahim Bagheri

2018-01-01

Lecture Notes in Computer Science (publié)

A Hierarchical Neural Attention-based Text Classifier

Koustuv Sinha

Yue Dong

Derek Ruths

Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to… (voir plus) extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.

2018-01-01

Conference on Empirical Methods in Natural Language Processing (publié)

World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions

Teng Long

Emmanuel Bengio

Ryan Lowe

Doina Precup

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading compr… (voir plus)ehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.

2017-09-01

Conference on Empirical Methods in Natural Language Processing (publié)

Predicting Success in Goal-Driven Human-Human Dialogues

Michael Noseworthy

Joelle Pineau

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.

2017-08-01

SIGDIAL Conference (publié)

Detecting Large Concept Extensions for Conceptual Analysis

L. Chartrand

Mohamed Bouguessa

2017-07-02

Machine Learning and Data Mining in Pattern Recognition (publié)

arxiv.org

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.

2017-03-08

Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (publié)

Computer-Assisted Conceptual Analysis of Textual Data as Applied to Philosophical Corpuses

Jean Guy Meunier

L. Chartrand