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 …

Artificial intelligence is changing the world, especially the interaction between machines and
humans. Learning and interpreting natural languages and responding have paved the way …

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 …

During the past decade, several areas of speech and language understanding have
witnessed substantial breakthroughs from the use of data-driven models. In the area of …

This paper proposes KB-InfoBot--a multi-turn dialogue agent which helps users search
Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented …

Training a task-completion dialogue agent via reinforcement learning (RL) is costly because
it requires many interactions with real users. One common alternative is to use a user …

In this paper, we study the problem of data augmentation for language understanding in task-
oriented dialogue system. In contrast to previous work which augments an utterance without …

Within the broad field of spoken dialogue systems, the application of machine-learning
approaches to dialogue management strategy design is a rapidly growing research area …

Despite widespread interests in reinforcement-learning for task-oriented dialogue systems,
several obstacles can frustrate research and development progress. First, reinforcement …

Evaluation is crucial in the development process of task-oriented dialogue systems. As an
evaluation method, user simulation allows us to tackle issues such as scalability and cost …