Featured Publications

Read our featured peer-reviewed publications, or view all publications.


May 2017

Advancing Machine Comprehension through question generation

While asking a question may seem straightforward, it is the process of asking the right question that can drive better understanding of concepts and information. While many QA datasets are geared to training for answering questions – an extractive task – the process of asking questions is comparatively abstractive: it requires the generation of text that may not appear in the context document. Asking ‘good’ questions involves skills beyond those needed to answer them.

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April 2017

Multi-Advisor Reinforcement Learning

In the first Maluuba paper following our acquisition by Microsoft, we deal with a novel branch of Separation of Concerns, called Multi-Advisor Reinforcement Learning (MAd-RL), where a single-agent RL problem is distributed to n learners, called advisors.

exploring  task decomposition and using multiple agents to handle subtasks 

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January 2017

Towards AGI: Building information-seeking agents

Our new paper and video outline a suite of tasks that teach artificial agents to accomplish tasks through efficient information-seeking behaviour. Such behaviour is a vital research step towards Artificial General Intelligence.

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December 2016

Frames: A Corpus For Adding Memory To Goal-Oriented Dialogue Systems

In this paper, study the role of memory in goal-oriented dialogue systems and we share a new dataset for the AI research community.

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December 2016

Improving Scalability of Reinforcement Learning by Separation of Concerns

In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task.

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