Timothy O'Donnell

Membre académique principal

odonnelt@mila.quebec

Chaire en IA Canada-CIFAR

Professeur adjoint, McGill University, Département de linguistique

Sujets de recherche

Modèles probabilistes

Théorie de l'information

Traitement du langage naturel

Site web

Biographie

Je suis professeur adjoint au Département de linguistique de l’Université McGill. Dans mes recherches, j'élabore des modèles mathématiques de généralisation, d'apprentissage et de traitement du langage. Mes travaux s'appuient sur les méthodes expérimentales en psychologie, les techniques de modélisation formelle utilisées dans le traitement du langage naturel, les outils théoriques de la linguistique, et les problématiques de ces trois domaines.

Étudiants actuels

Amanda Doucette

Doctorat - McGill

Benjamin LeBrun

Collaborateur·rice alumni - McGill

Site web

Github

Google Scholar

Publications

Recursive Top-Down Production for Sentence Generation with Latent Trees

Shawn Tan

Yikang Shen

Timothy O'Donnell

Alessandro Sordoni

Aaron Courville

2020-11-01

Findings of the Association for Computational Linguistics: EMNLP 2020 (publié)

doi.org

arxiv.org

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach

Wenyu Du

Zhouhan Lin

Yikang Shen

Timothy O'Donnell

Yoshua Bengio

Yue Sara Zhang

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally eff… (voir plus)iciently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called “syntactic distances”, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.

2020-07-01

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (publié)

doi.org

arxiv.org

CLOSURE: Assessing Systematic Generalization of CLEVR Models

Dzmitry Bahdanau

Harm de Vries

Timothy O'Donnell

Shikhar Murty

Philippe Beaudoin

Yoshua Bengio

Aaron Courville