Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2020-2021)

In the space of a few years, machine learning has moved from an academic discipline pursued by a small group of dedicated researchers, to a mainstream industry, poised to solve any and all problems where data is available. At the core of this revolution are a set of flexible, robust and powerful algorithms, many of them based on neural network and deep learning architectures. One particularly promising direction for the deployment of deep learning technologies is in building language understanding and generation systems.

Our work aims to extend deep learning methods to tackle challenging tasks in natural language understanding and generation by exploiting the reinforcement learning paradigm.
Reinforcement learning (RL) provides a rich mathematical and algorithmic framework for tackling the problem of sequential decision-making with an unknown (or partially known) system.

This project focuses primarily on the development of new reinforcement learning methods for conversational systems, and in particular for deployment in Dialogue Management systems. This domain is chosen due to the sequential and interactive nature of the task, and the possibility for feedback to occur at different time points, which are a good match for the RL setting.

In Dialogue Management, the main goal is to build an AI agent that can converse with a human user using natural language. Such systems can be used to allow different types of interactions between a machine and user: social chatting, acquiring information (e.g., customer service), conducting transactions (e.g., online banking or reservation system). Eventually, a complete system can incorporate several of these aspects, allowing a rich and frequent pattern of interactions. In terms of our objectives, the Dialogue Management task is a classic example of an RL problem, as it requires the machine to learn a good strategy for producing a sequence of dialogue acts (responses) throughout the conversation.

Our goal over the next five years is to develop a range of RL models and algorithms suitable for deployment in dialogue systems, with specific examples and benchmark results of their performance quality. We also aim to develop the next generation of ML researchers with graduate-level training at the intersection of reinforcement learning, deep learning and NLP.