Joelle Pineau

Membre académique principal

jpineau@mila.quebec

Chaire en IA Canada-CIFAR

Professeure agrégée, McGill University, École d'informatique

Co-directrice générale, Meta AI (FAIR - Facebook AI Research)

Sujets de recherche

Apprentissage automatique médical

Apprentissage par renforcement

Traitement du langage naturel

Site web

Google Scholar

Biographie

Joelle Pineau est professeure agrégée et titulaire d’une bourse William Dawson à l'Université McGill, où elle codirige le Laboratoire de raisonnement et d'apprentissage. Elle est membre du corps professoral de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Elle est également vice-présidente de la recherche en IA chez Meta (anciennement Facebook), où elle dirige l'équipe FAIR (Fundamental AI Research). Elle détient un baccalauréat ès sciences en génie de l'Université de Waterloo et une maîtrise et un doctorat en robotique de l'Université Carnegie Mellon.

Ses recherches sont axées sur le développement de nouveaux modèles et algorithmes pour la planification et l'apprentissage dans des domaines complexes partiellement observables. Elle travaille également sur l'application de ces algorithmes à des problèmes complexes en robotique, dans les soins de santé, dans les jeux et dans les agents conversationnels. Elle est membre du comité de rédaction du Journal of Artificial Intelligence Research et du Journal of Machine Learning Research, et est actuellement présidente de l'International Machine Learning Society. Elle a été lauréate de la bourse commémorative E. W. R. Steacie du Conseil de recherches en sciences naturelles et en génie (CRSNG) 2018 et du Prix du Gouverneur général pour l'innovation 2019. Elle est membre de l'Association pour l'avancement de l'intelligence artificielle (AAAI), membre principal de l'Institut canadien de recherches avancées (CIFAR) et membre de la Société royale du Canada.

Étudiants actuels

Arnaud Bergeron

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Nicolas Le Roux

Ximeng Mao

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Collaborateur·rice alumni - McGill

Publications

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Iulian V. Serban

Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterance… (voir plus)s in a dialogue. In an effort to model this kind of generative process, we propose a neural network-based generative architecture, with latent stochastic variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with recent neural network architectures. We evaluate the model performance through automatic evaluation metrics and by carrying out a human evaluation. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate the generation of long outputs and maintain the context.

2017-02-11

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

Iulian Vlad Serban

Chia-Wei Liu

In this paper, we construct and train end-to-end neural network-based dialogue systems usingan updated version of the recent Ubuntu Dialogue… (voir plus) Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines in two different environments: one where models are trained to maximize the log-likelihood of a generated utterance conditioned on the context of the conversation, and one where models are trained to select the correct next response from a list of candidate responses. These are both evaluated on a recall task that we call Next Utterance Classification (NUC), as well as other generation-specific metrics. Finally, we provide a qualitative error analysis to help determine the most promising directions for future research on the Ubuntu Dialogue Corpus, and for end-to-end dialogue systems in general.

2017-01-19

Dialogue & Discourse (publié)

doi.org

An Actor-Critic Algorithm for Sequence Prediction

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Curren… (voir plus)t log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a \textit{critic} network that is trained to predict the value of an output token, given the policy of an \textit{actor} network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling.

2016-12-31

ICLR.cc/2017/conference (poster)

doi.org

openreview.net

Independently Controllable Factors

Valentin Thomas

Jules Pondard

Emmanuel Bengio

Marc Sarfati

Philippe Beaudoin

Marie-Jean Meurs

Joelle Pineau

Doina Precup

Yoshua Bengio

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.

2016-12-31

arXiv (prépublication)

doi.org

arxiv.org

Independently Controllable Features

Valentin Thomas

2016-12-31

(publié)

doi.org

arxiv.org

Multitask Spectral Learning of Weighted Automata.

Guillaume Rabusseau

Borja Balle

Joelle Pineau

2016-12-31

Neural Information Processing Systems (inconnu)

dblp.uni-trier.de

Piecewise Latent Variables for Neural Variational Text Processing

Iulian V. Serban

Alexander G. Ororbia II

Joelle Pineau

Aaron Courville

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variable… (voir plus)s, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.

2016-12-31

Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Ryan Lowe

Michael Noseworthy

Iulian V. Serban

Nicolas A.-Gontier

Yoshua Bengio

Joelle Pineau

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automa… (voir plus)tic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.

2016-12-31

Annual Meeting of the Association for Computational Linguistics (publié)

doi.org

arxiv.org

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

Iulian V. Serban

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Ge… (voir plus)nerative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

2016-03-04

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Recent Advances in Reinforcement Learning

Joelle Pineau

2007-12-31

Lecture Notes in Computer Science (publié)

doi.org

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Boussole des politiques en IA

Joelle Pineau

Biographie

Étudiants actuels

Publications

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Boussole des politiques en IA

Mots-clés populaires:

Joelle Pineau

Biographie

Étudiants actuels

Publications