|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4973641||1451680||2018||19 صفحه PDF||سفارش دهید||دانلود کنید|
- We propose a new methodology to improve turn-taking capabilities in a Spoken Dialogue System using Reinforcement Learning.
- We describe a new incremental Dialogue System Architecture and a new incremental dialogue simulator.
- We train a Reinforcement Learning policy with the simulator.
- We show that it outperforms the handcrafted and the non-incremental baseline strategies.
- We validate these results in a live study with real users.
This article introduces a new methodology to enhance an existing traditional Spoken Dialogue System (SDS) with optimal turn-taking capabilities in order to increase dialogue efficiency. A new approach for transforming the traditional dialogue architecture into an incremental one at a low cost is presented: a new turn-taking decision module called the Scheduler is inserted between the Client and the Service. It is responsible for handling turn-taking decisions. Then, a User Simulator which is able to interact with the system using this new architecture has been implemented and used to train a new Reinforcement Learning turn-taking strategy. Compared to a non-incremental and a handcrafted incremental baselines, it is shown to perform better in simulation and in a real live experiment.
Journal: Computer Speech & Language - Volume 47, January 2018, Pages 93-111