<?xml version="1.0" standalone="yes"?>
<Paper uid="P04-1010">
  <Title>Data-Driven Strategies for an Automated Dialogue System</Title>
  <Section position="3" start_page="0" end_page="0" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> Relevant human-computer dialogue research efforts include the TRAINS project and the DARPA Communicator program.</Paragraph>
    <Paragraph position="1"> The classic TRAINS natural-language dialogue project (Allen et al., 1995) is a plan-based system which requires a detailed model of the domain and therefore cannot be used for a wide-ranging application such as financial services.</Paragraph>
    <Paragraph position="2"> The US DARPA Communicator program has been instrumental in bringing about practical implementations of spoken dialogue systems.</Paragraph>
    <Paragraph position="3"> Systems developed under this program include CMU's script-based dialogue manager, in which the travel itinerary is a hierarchical composition of frames (Xu and Rudnicky, 2000). The AT&amp;T mixed-initiative system uses a sequential decision process model, based on concepts of dialog state and dialog actions (Levin et al., 2000). MIT's Mercury flight reservation system uses a dialogue control strategy based on a set of ordered rules as a mechanism to manage complex interactions (Seneff and Polifroni, 2000). CU's dialogue manager is event-driven, using a set of hierarchical forms with prompts associated with fields in the forms. Decisions are based not on scripts but on current context (Ward and Pellom, 1999).</Paragraph>
    <Paragraph position="4"> Our data-driven strategy is similar in spirit to that of CU. We take a statistical approach, in which a large body of transcribed, annotated conversations forms the basis for task identification, dialogue act recognition, and form filling for task completion.</Paragraph>
  </Section>
class="xml-element"></Paper>