Attracted by their easy-to-use interfaces and captivating benefits, conversational systems
have been widely embraced by many individuals and organizations as side-by-side digital …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …

We present NLU++, a novel dataset for natural language understanding (NLU) in task-
oriented dialogue (ToD) systems, with the aim to provide a much more challenging …

User Simulators are one of the major tools that enable offline training of task-oriented
dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The …

Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation.
Traditional RL algorithms, however, fail to scale to large domains due to the curse of …

Dialogue policy plays an important role in task-oriented spoken dialogue systems. It
determines how to respond to users. The recently proposed deep reinforcement learning …

Task-oriented personal assistants enable people to interact with a host of devices and
services using natural language. One of the challenges of making neural dialogue systems …

Traditional dialogue policy needs to be trained independently for each dialogue task. In this
work, we aim to solve a collection of independent dialogue tasks using a unified dialogue …

Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting
language actions based on the current dialog state. Deep reinforcement learning methods …