Data-Driven Strategies for an Automated Dialogue System

Data-Driven Strategies for an Automated Dialogue System Hilda HARDY, Tomek STRZALKOWSKI, Min WU ILS Institute University at Albany, SUNY 1400 Washington Ave., SS262 Albany, NY 12222 USA hhardy|tomek|minwu@ cs.albany.edu Cristian URSU, Nick WEBB Department of Computer Science University of Sheffield Regent Court, 211 Portobello St. Sheffield S1 4DP UK c.ursu@sheffield.ac.uk, n.webb@dcs.shef.ac.uk Alan BIERMANN, R. Bryce INOUYE, Ashley MCKENZIE Department of Computer Science Duke University P.O. Box 90129, Levine Science Research Center, D101 Durham, NC 27708 USA awb|rbi|armckenz@cs.duke.edu 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. 1 Introduction Recently there has been a great deal of interest in improving natural-language human-computer conversation. Automatic speech recognition continues to improve, and dialogue management techniques have progressed beyond menu-driven prompts and restricted customer responses. Yet few researchers have made use of a large body of human-human telephone calls, on which to form the basis of a data-driven automated system. The Amitiés project seeks to develop novel technologies for building empirically induced dialogue processors to support multilingual human-computer interaction, and to integrate these technologies into systems for accessing information and services (http://www.dcs.shef.ac. uk/nlp/amities). Sponsored jointly by the European Commission and the US Defense Advanced Research Projects Agency, the Amitiés Consortium includes partners in both the EU and the US, as well as financial call centers in the UK and France. A large corpus of recorded, transcribed telephone conversations between real agents and customers gives us a unique opportunity to analyze and incorporate features of human-human dialogues into our automated system. (Generic names and numbers were substituted for all personal details in the transcriptions.) This corpus spans two different application areas: software support and (a much smaller size) customer banking. The banking corpus of several hundred calls has been collected first and it forms the basis of our initial multilingual triaging application, implemented for English, French and German (Hardy et al., 2003a); as well as our prototype automatic financial services system, presented in this paper, which completes a variety of tasks in English. The much larger software support corpus (10,000 calls in English and French) is still being collected and processed and will be used to develop the next Amitiés prototype. We observe that for interactions with structured data – whether these data consist of flight information, spare parts, or customer account information – domain knowledge need not be built ahead of time. Rather, methods for handling the data can arise from the way the data are organized. Once we know the basic data structures, the transactions, and the protocol to be followed (e.g., establish caller’s identity before exchanging sensitive information); we need only build dialogue models for handling various …

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