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| 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. |
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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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