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