Most of today's task-based spoken dialogue systems perform poorly if the user goal is not within the system's task domain. On the other hand, chatbots cannot perform tasks involving robot actions but are able to deal with unforeseen user input. To overcome the limitations of each of these separate approaches and be able to exploit their strengths, we present and evaluate a fully autonomous robotic system using a novel combination of task-based and chat-style dialogue in order to enhance the user experience with human-robot dialogue systems. We employ Reinforcement Learning (RL) to create a scalable and extensible approach to combining chat and task-based dialogue for multimodal systems. In an evaluation with real users, the combined system was rated as significantly more “pleasant” and better met the users' expectations in a hybrid task+chat condition, compared to the task-only condition, without suffering any significant loss in task completion.
|Name||IEEE International Symposium on Robot and Human Interactive Communication|
|Conference||26th IEEE International Symposium on Robot and Human Interactive Communication 2017|
|Abbreviated title||RO-MAN 2017|
|Period||28/08/17 → 1/09/17|