Data-driven strategies for an automated dialogue system

Authors:
Hilda Hardy;Tomek Strzalkowski;Min Wu;Cristian Ursu;Nick Webb;Alan Biermann;R. Bryce Inouye;Ashley McKenzie
Affiliations:
University at Albany, Albany, NY;University at Albany, Albany, NY;University at Albany, Albany, NY;University of Sheffield, Sheffield, UK;University of Sheffield, Sheffield, UK;Duke University, Durham, NC;Duke University, Durham, NC;Duke University, Durham, NC
Venue:
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Year:
2004

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Abstract

We present a prototype natural-language problem-solving application for a financial services call center, developed as part of the Amitiés multilingual human-computer dialogue project. Our automated dialogue system, based on empirical evidence from real call-center conversations, features a data-driven approach that allows for mixed system/customer initiative and spontaneous conversation. Preliminary evaluation results indicate efficient dialogues and high user satisfaction, with performance comparable to or better than that of current conversational travel information systems.