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<Paper uid="P97-1034">
  <Title>Tracking Initiative in Collaborative Dialogue Interactions</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Naturally-occurring collaborative dialogues are very rarely, if ever, one-sided. Instead, initiative of the interaction shifts among participants in a primarily principled fashion, signaled by features such as linguistic cues, prosodic cues and, in face-to-face interactions, eye gaze aad gestures. Thus, for a dialogue system to interact with its user in a natural and coherent manner, it must recognize the user's cues for initiative shifts and provide appropriate cues in its responses to user utterances.</Paragraph>
    <Paragraph position="1"> Previous work on mixed-initiative dialogues focused on tracking a single thread of control among participants.</Paragraph>
    <Paragraph position="2"> We argue that this view of initiative fails to distinguish between task initiative and dialogue initiative, which together determine when and how an agent will address an issue. Although physical cues, such as gestures and eye gaze, play an important role in coordinating initiative shifts in face-to-face interactions, a great deal of information regarding initiative shifts can be extracted from utterances based on linguistic and domain knowledge alone. By taking into account such cues during dialogue interactions, the system is better able to determine the task and dialogue initiative holders for each turn and to tailor its response to user utterances accordingly.</Paragraph>
    <Paragraph position="3"> In this paper, we show how distinguishing between task and dialogue initiatives accounts for phenomena in collaborative dialogues that previous models were unable to explain. We show that a set of cues, which can be recognized based on linguistic and domain knowledge alone, can be utilized by a model for tracking initiative to predict the task and dialogue initiative holders with 99.1% and 87.8% accuracies, respectively, in collaborative planning dialogues. Furthermore, application of our model to dialogues in various other collaborative environments consistently increases the accuracies in the prediction of task and dialogue initiative holders by 2-4 and 8-13 percentage points, respectively, compared to a simple prediction method without the use of cues, thus illustrating the generality of our model.</Paragraph>
  </Section>
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