In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal
Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is …

Offline methods for reinforcement learning have a potential to help bridge the gap between
reinforcement learning research and real-world applications. They make it possible to learn …

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …

Learning to communicate through interaction, rather than relying on explicit supervision, is
often considered a prerequisite for developing a general AI. We study a setting where two …

In this work, we present a hybrid learning method for training task-oriented dialogue systems
through online user interactions. Popular methods for learning task-oriented dialogues …

Conversational recommender systems (CRSs) assist online users in their information-
seeking and decision making tasks by supporting an interactive process. Although these …