Chris Pal

Biography

Christopher Pal is a Canada CIFAR AI Chair, full professor at Polytechnique Montréal and adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Distinguished Scientist at ServiceNow Research.

Pal has been involved in AI and machine learning research for over twenty-five years and has published extensively on large-scale language modelling methods and generative modelling techniques. He has a PhD in computer science from the University of Waterloo.

Current Students

Mai Ababneh

Research Intern - McGill University

ababneh.mai@gmail.com

Shubham Agarwal

Postdoctorate - HEC Montréal

Principal supervisor :

Paul Barde

Collaborating researcher - McGill University

Principal supervisor :

Derek Nowrouzezahrai

paul.b.barde@gmail.com

Master's Research - Université de Montréal

Chris Beckham

PhD - Polytechnique Montréal

Can (Sam) Chen

PhD - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

PhD - Polytechnique Montréal

Chris Emezue

Master's Research - Université de Montréal

Co-supervisor :

Collaborating Alumni - Polytechnique Montréal

Roger Girgis

PhD - Polytechnique Montréal

Florian Golemo

Postdoctorate - McGill University

Co-supervisor :

Master's Research - Polytechnique Montréal

PhD - Université de Montréal

Co-supervisor :

Collaborating researcher - Université de Montréal

Master's Research - Université de Montréal

Olga Luo

PhD - Université de Montréal

Joel Moniz

PhD - Polytechnique Montréal

Jonathan Pilault

PhD - Polytechnique Montréal

Juan Rodriguez

PhD - École de technologie suprérieure

Luke Rowe

PhD - Université de Montréal

Principal supervisor :

Gaurav Sahu

Postdoctorate - HEC Montréal

Principal supervisor :

PhD - Polytechnique Montréal

Principal supervisor :

PhD - McGill University

Principal supervisor :

PhD - Polytechnique Montréal

Direct Behavior Specification via Constrained Reinforcement Learning

Blog Posts

August 31, 2022

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

Sandeep Subramanian

Adam Trischler

A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on l… (see more)arge amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.

2018-01-01

ICLR (Poster) (published)

openreview.net

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

Nan Rosemary Ke

Anirudh Goyal

Olexa Bilaniuk

Jonathan Binas

Michael Curtis Mozer

Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most common me… (see more)thod for training recurrent neural networks, back-propagation through time (BPTT), requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. This becomes computationally expensive or even infeasible when used with long sequences. Importantly, biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states (consider days, months, or years.) However, humans are often reminded of past memories or mental states which are associated with the current mental state. We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state. Based on this principle, we study a novel algorithm which only back-propagates through a few of these temporal skip connections, realized by a learned attention mechanism that associates current states with relevant past states. We demonstrate in experiments that our method matches or outperforms regular BPTT and truncated BPTT in tasks involving particularly long-term dependencies, but without requiring the biologically implausible backward replay through the whole history of states. Additionally, we demonstrate that the proposed method transfers to longer sequences significantly better than LSTMs trained with BPTT and LSTMs trained with full self-attention.

Towards Deep Conversational Recommendations

Raymond Li

Samira Ebrahimi Kahou

Hannes Schulz

Vincent Michalski

Laurent Charlin

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendat… (see more)ion is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

Towards Text Generation with Adversarially Learned Neural Outlines

Sandeep Subramanian

Sai Rajeswar

Alessandro Sordoni

Adam Trischler

Recent progress in deep generative models has been fueled by two paradigms -- autoregressive and adversarial models. We propose a combinatio… (see more)n of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.

Twin Networks: Matching the Future for Sequence Generation

Dmitriy Serdyuk

Nan Rosemary Ke

Alessandro Sordoni

Adam Trischler

We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given seq… (see more)uence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference. We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states). We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.

2018-01-01

ICLR (Poster) (published)

openreview.net

ACtuAL: Actor-Critic Under Adversarial Learning

Anirudh Goyal

Nan Rosemary Ke

Alex Lamb

Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs ar… (see more)e typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and realistic images. However, a major limitation of GANs is that training relies on passing gradients from the discriminator through the generator via back-propagation. This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function. These difficulties extend to the reinforcement learning setting when the action space is composed of discrete decisions. We address these issues by reframing the GAN framework so that the generator is no longer trained using gradients through the discriminator, but is instead trained using a learned critic in the actor-critic framework with a Temporal Difference (TD) objective. This is a natural fit for sequence modeling and we use it to achieve improvements on language modeling tasks over the standard Teacher-Forcing methods.

2017-11-13

ArXiv (preprint)

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

Nan Rosemary Ke

Anirudh Goyal

Olexa Bilaniuk

Jonathan Binas

Laurent Charlin

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagat… (see more)e credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and truncated backpropagation through time is used as a heuristic. However, this usually leads to biased estimates of the gradient in which longer term dependencies are ignored. Addressing this issue, we propose an alternative algorithm, Sparse Attentive Backtracking, which might also be related to principles used by brains to learn long-term dependencies. Sparse Attentive Backtracking learns an attention mechanism over the hidden states of the past and selectively backpropagates through paths with high attention weights. This allows the model to learn long term dependencies while only backtracking for a small number of time steps, not just from the recent past but also from attended relevant past states.

2017-11-07

ArXiv (preprint)

Adversarial Generation of Natural Language

Sandeep Subramanian

Sai Rajeswar

Francis Dutil

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for … (see more)image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.

2017-08-01

Proceedings of the 2nd Workshop on Representation Learning for NLP (published)

doi.org

Self-organized Hierarchical Softmax

Yikang Shen

Shawn Tan

We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead … (see more)of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.

2017-07-26

ArXiv (preprint)

A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering

Tegan Maharaj

Nicolas Ballas

Anna Rohrbach

While deep convolutional neural networks frequently approach or exceed human-level performance in benchmark tasks involving static images, e… (see more)xtending this success to moving images is not straightforward. Video understanding is of interest for many applications, including content recommendation, prediction, summarization, event/object detection, and understanding human visual perception. However, many domains lack sufficient data to explore and perfect video models. In order to address the need for a simple, quantitative benchmark for developing and understanding video, we present MovieFIB, a fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models predictions, and compare these with human performance. We investigate the relative importance of language, static (2D) visual features, and moving (3D) visual features, the effects of increasing dataset size, the number of frames sampled, and of vocabulary size. We illustrate that: this task is not solvable by a language model alone, our model combining 2D and 3D visual information indeed provides the best result, all models perform significantly worse than human-level. We provide human evaluation for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgment. We suggest avenues for improving video models, and hope that the MovieFIB challenge can be useful for measuring and encouraging progress in this very interesting field.

2017-07-21

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (published)

doi.org

Movie Description

Anna Rohrbach

Atousa Torabi

Marcus Rohrbach

Niket Tandon

Hugo Larochelle