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<Paper uid="P06-4007">
  <Title>FERRET: Interactive Question-Answering for Real-World Environments</Title>
  <Section position="3" start_page="0" end_page="25" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> As the accuracy of today's best factoid question-answering (Q/A) systems (Harabagiu et al., 2005; Sun et al., 2005) approaches 70%, research has begun to address the challenges of integrating automatic Q/A systems into real-world environments.</Paragraph>
    <Paragraph position="1"> A new class of applications known as interactive Q/A systems are now being developed which allow users to ask questions in the context of extended dialogues in order to gather information related to any number of complex scenarios. In this paper, we describe our interactive Q/A system known as FERRET which uses an approach based on predictive questioning in order to meet the changing information needs of users over the course of a Q/A dialogue.</Paragraph>
    <Paragraph position="2"> Answering questions in an interactive setting poses three new types of challenges for traditional Q/A systems. First, since current Q/A systems are designed to answer single questions in isolation, interactive Q/A systems must look for ways to foster interaction with a user throughout all phases of the research process. Unlike traditional Q/A applications, interactive Q/A systems must do more than cooperatively answer a user's single question.</Paragraph>
    <Paragraph position="3"> Instead, in order to keep a user collaborating with the system, interactive Q/A systems need to provide access to new types of information that are somehow relevant to the user's stated and unstated information needs.</Paragraph>
    <Paragraph position="4"> Second, we have found that users of Q/A systems in real-world settings often ask questions that are much more complex than the types of factoid questions that have been evaluated in the annual Text Retrieval Conference (TREC) evaluations. When faced with a limited period of time to gather information, even experienced users of Q/A may nd it dif cult to translate their information needs into the simpler types of questions that Q/A systems can answer. In order to provide effective answers to these questions, interactive question-answering systems need to include question decomposition techniques that can break down complex questions into the types of simpler factoid-like questions that traditional Q/A systems were designed to answer.</Paragraph>
    <Paragraph position="5"> Finally, interactive Q/A systems must be sensitive not only to the content of a user's question but also to the context that it is asked in. Like other types of task-oriented dialogue systems, interactive Q/A systems need to model both what a user knows and what a user wants to know over the course of a Q/A dialogue: systems that fail to represent a user's knowledge base run the risk of returning redundant information, while systems that do not model a user's intentions can end up returning irrelevant information.</Paragraph>
    <Paragraph position="6"> In the rest of this paper, we discuss how the FERRET interactive Q/A system currently addresses the rst two of these three challenges.</Paragraph>
  </Section>
class="xml-element"></Paper>