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<Paper uid="H89-1037">
  <Title>Elements of a Computational Model of Cooperative Response Generation*</Title>
  <Section position="2" start_page="0" end_page="216" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Our success in day-to-day affairs depends to a great extent on the cooperation of those with whom we interact. Studies of man-machine interaction show that we expect the same from the (complex) systems we deal with \[14\]. Here we consider the case of natural language question-answering (NLQA) systems. To be cooperative, any system must recognize and accommodate the goals, plans, and needs of its users. For NLQA systems, this means that they must take the initiative when responding, rather than answering queries passively. They cannot simply translate input queries into transactions on database or expert systems--they have to apply many more complex reasoning mechanisms to the task of deciding how to respond. It has been suggested that cooperative NLQA systems must be able to provide extended responses \[15, 16\], combining such elements as:  including Kaplan's work on responding when presuppositions fail \[7\], Mays' work both on responding when queries fail intensionally \[8\] and on determining competent monitor offers \[9\], McKeown's TEXT system for explaining concepts known to a database system \[11\], McCoy's system for correcting object-related misconceptions \[10\], Hirschberg's work on scalar implicatures and their use in avoiding the production of misleading responses \[5\], and Pollack's plan inference model for recognizing and responding to discrepancies between the system's and the user's beliefs about domain plans and actions \[12\]. Other explorations of cooperative communication include \[1\], \[2\], \[4\], \[6\], \[13\], \[15\], and \[17\]. For more complete references, the reader is referred to Cheikes \[3\].</Paragraph>
    <Paragraph position="1"> The results of these studies have been highly informative. Many different kinds of cooperative behavior have seen identified, and computational models of them proposed. What is of interest to us here is the fact that all efforts to date in this area share the same implicit assumption--that cooperative response generation can be decomposed into separate reasoning processes. But this &amp;quot;decomposability assumption&amp;quot; in turn raises the inlegralion problem--the problem of getting those elements to work together in the production of a single response. This so far has largely has been ignored.</Paragraph>
    <Paragraph position="2"> Solving the integration problem means devising an architecture for cooperative response generalion (CRG) systems--NLQA systems that can combine in their responses instances of different kinds of cooperative behavior appropriate to the situation. Now that the study of cooperativeness in natural language is beyond its infancy (although still far from mature), it is an appropriate time to confront the integration problem and study the design of CRG systems. This paper describes the beginnings of such a computational model of CRG.</Paragraph>
  </Section>
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