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<Paper uid="P94-1009">
  <Title>A HYBRID REASONING MODEL FOR INDIRECT ANSWERS</Title>
  <Section position="2" start_page="0" end_page="58" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> Imagine a discourse context for (1) in which R's use of just (ld) is intended to convey a No, i.e., that R is not going shopping tonight. (By convention, square brackets indicate that the enclosed text was not explicitly stated.) The part of R's response consisting of (ld) - (le) is what we call an indirect answer to a Yes-No question, and if (lc) had been uttered, (lc) would have been called a direct answer.</Paragraph>
    <Paragraph position="1">  l.a. Q: I need a ride to the mall.</Paragraph>
    <Paragraph position="2"> b. Are you going shopping tonight? c. R: \[no\] d. My car's not running.</Paragraph>
    <Paragraph position="3"> e. The rear axle is broken.</Paragraph>
    <Paragraph position="4">  According to one study of spoken English \[Stenstrhm, 1984\], 13 percent of responses to Yes-No questions were indirect answers. Thus, the ability to interpret indirect answers is required for robust dialogue systems. Furthermore, there are good reasons for generating indirect answers instead of just yes, no, or I don't know. First, they may provide information which is needed to avoid misleading the questioner \[Hirschberg, 1985\]. Second, they contribute to an efficient dialogue by anticipating follow-up questions. Third, they may be used for social reasons, as in (1).</Paragraph>
    <Paragraph position="5"> This paper provides a computational model for the interpretation and generation of indirect answers to Yes-No questions in English. More precisely, by a Yes-No question we mean one or more utterances used as a request by Q (the questioner) that R (the responder) convey R's evaluation of the truth of a proposition p. An indirect answer implicitly conveys via one or more utterances R's evaluation of the truth of the questioned proposition p, i.e. that p is true, that p is false, that there is some truth to p, that p may be true, or that p may be false. Our model presupposes that Q's question has been understood by R as intended by Q, that Q's request was appropriate, and that Q and R are engaged in a cooperative goal-directed dialogue. The interpretation and generation components of the model have been implemented in Common Lisp on a Sun SPARCstation.</Paragraph>
    <Paragraph position="6"> The model employs an agent's pragmatic knowledge of how language typically is used to answer Yes-No questions in English to constrain the process of generating and interpreting indirect answers. This knowledge is encoded as a set of domain-independent discourse plan operators and a set of coherence rules, described in section 2.1 Figure 1 shows the architecture of our system. It is reversible in that the same pragmatic knowledge is used by the interpretation and generation modules. The interpretation algorithm, described in section 3, is a hybrid approach employing both plan inference and logical inference to infer R's discourse plan. The generation algorithm, described in section 4, constructs R's discourse plan in two phases. During the first phase, stimulus conditions are used to trigger goals to include appropriate, extra information in the response plan. In the second phase, the response plan is pruned to eliminate parts which can be inferred by Q.</Paragraph>
    <Paragraph position="7"> hOur main sources of data were previous studies \[Hirschberg, 1985, Stenstrhm, 1984\], transcripts of naturally occurring two-person dialogue \[American Express transcripts, 1992\], and constructed examples.</Paragraph>
  </Section>
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