<?xml version="1.0" standalone="yes"?>
<Paper uid="W91-0115">
  <Title>Handling Pragmatic Information With A Reversible Architecture</Title>
  <Section position="2" start_page="0" end_page="119" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Reversibility or bi-directionality of grammars seems to play a quite important role in natural language procesSing. It reduces the cost of constructing a grammar; we need to use only one grammar instead of two for parsing and generation. Cost ~here includes not only the making of grammar rules but also verifying the rules and the algorithms for parsing and generation. Reversibility differs from bidirectionality: the former requires the same mechanism and 'grammar for parsing and generation; the latter requires just the same grammar as shown Figure l(Noord, 1990).</Paragraph>
    <Paragraph position="1"> Pragmatic in:formation is not rigidly defined; rather it is thought of as information other than syntaqtic and semantic information. It is indispetlsable for explaining many linguistic pheno/nena from cleft sentences to discourse structures. As pragmatics cannot restrain language in a strict manner like syntax, it must be processed differently; that is, a distinction must be made between constraints that need to be fully satisfied and those that do not.'Plan representation seems to be appropriate for collecting different level information. A plan consists of precon ditions, constraints, plan expansion (usually termed the body), and effects. The relationship between preconditions and constraints parallels that between pragmatic and syntactic information. Thus, the difference between preconditions and constraints can be easily modeled.</Paragraph>
    <Paragraph position="2"> Handling pragmatic information clearly depends on assumption of belief: Generating referring expressions requires inferencing the hearer's belief (Appelt, 1985); Producing text requires the usage of a one-sided mutual beliefl(Moore et a1.,1989); the listener's inference about the speaker's belief greatly helps to resolve anaphora or to analyze the speaker's intention. In any case, belief becomes a condition for further inference; however, it is difficult if not impossible to confirm the assumed belief. Thus, a new mechanism based on a new architecture is needed. Approximate reasoning (Elkan,1990) is statable for this purpose. Processing can con&amp;quot;tinue, even if some preconditions are not fully satisfied; they are held as assumptions 2. This approach seems to be very natural. For example, in conversations, the speaker should conceptualize the listener's 1One-sided mutual belief is one half of mutual knowledge, so to speak, namely the set of those pieces of mutual knowledge that constitute the knowledge of one speaker (Bunt, 1989: 60).</Paragraph>
    <Paragraph position="3"> 2Since approximate reasoning can fail, assumptions must be held explicitly for further inference. Plan representation is adequate for that reason.</Paragraph>
    <Section position="1" start_page="119" end_page="119" type="sub_section">
      <SectionTitle>
Strings Stri0gs
Analyzer Forms
</SectionTitle>
      <Paragraph position="0"> understanding. In most conversations, however, the speaker does not keep confirming the other's belief because this would disrupt the conversation flow.</Paragraph>
      <Paragraph position="1"> This paper describes a reversible architecture to handle not only syntactic and semantic information, but also pragmatic information. Existing architectures cannot represent pragmatic information explicitly, and lack reasoning capability given insufficient information. I argue that the techniques of plan representation and approximate reasoning in the argumentation system introduced here are effective for solving these problems.</Paragraph>
      <Paragraph position="2"> First, the difficulties of existing architectures have in handling pragmatic information are described. Next, plan representation of linguistic information and approximate reasoning are mentioned in the context of the argumentation system. Third, parsing and generation examples are shown. Finally, the problem of proposed data structure, the decomposition of semantic representation, the role of syntactic information and the difference between active and passive vocabulary are discussed.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>