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Academic Paper

Title: Learning Effective and Engaging Strategies for Advice-Giving Human-Machine Dialogue
Author: Martijn Spitters
Institution: Textkernel BV
Author: Marco De Boni
Email: click here TO access email
Institution: Unilever Corporate Research
Author: Jakub Zavrel
Institution: Textkernel BV
Author: Remko Bonnema
Institution: Textkernel BV
Linguistic Field: Computational Linguistics
Abstract: We describe a system that automatically learns effective and engaging dialogue strategies, generated from a library of dialogue content, using reinforcement learning from user feedback. Besides the more usual clarification and verification components of dialogue, this library contains various social elements like greetings, apologies, small talk, relational questions and jokes. We tested the method through an experimental dialogue system that encourages take-up of exercise and shows that the learned dialogue policy performs as well as one built by human experts for this system.


This article appears IN Natural Language Engineering Vol. 15, Issue 3, which you can READ on Cambridge's site or on LINGUIST .

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