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<Paper uid="P04-1010">
  <Title>Data-Driven Strategies for an Automated Dialogue System</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
    <Paragraph position="1"> The Amities 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.</Paragraph>
    <Paragraph position="2"> uk/nlp/amities). Sponsored jointly by the European Commission and the US Defense Advanced Research Projects Agency, the Amities 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 Amities prototype.</Paragraph>
    <Paragraph position="3"> 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 conversational situations, in order to implement a dialogue system. For our corpus, we have used a modified DAMSL tag set (Allen and Core, 1997) to capture the functional layer of the dialogues, and a frame-based semantic scheme to record the semantic layer (Hardy et al., 2003b). The &amp;quot;frames&amp;quot; or transactions in our domain are common customer-service tasks: VerifyId, ChangeAddress, InquireBalance, Lost/StolenCard and Make Payment. (In this context &amp;quot;task&amp;quot; and &amp;quot;transaction&amp;quot; are synonymous.) Each frame is associated with attributes or slots that must be filled with values in no particular order during the course of the dialogue; for example, account number, name, payment amount, etc.</Paragraph>
  </Section>
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