کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4973641 1365496 2018 19 صفحه PDF ندارد دانلود کنید
عنوان انگلیسی مقاله
A methodology for turn-taking capabilities enhancement in Spoken Dialogue Systems using Reinforcement Learning☆
ترجمه فارسی عنوان
یک روش برای تقویت قابلیت های نوبت در سیستم های مباحثه گفتاری با استفاده از یادگیری تقویتی
کلمات کلیدی
سیستم گفتمان گفتاری؛ تبدیل شدن؛ گفتگو افزایشی؛ یادگیری تقویتی؛
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

•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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 47, January 2018, Pages 93-111
نویسندگان
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