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<Paper uid="W97-0403">
  <Title>Towards Translating Spoken Language Pragmatics in an Analogical Framework</Title>
  <Section position="4" start_page="0" end_page="16" type="intro">
    <SectionTitle>
2 Previous Approaches: Recognizing
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Speech Act Types
</SectionTitle>
      <Paragraph position="0"> For the reasons outlined above, it is important to handle pragmatic information in spoken language translation. The most studied area in pragmatics has been the illocutionary force of utterances. This type of information has been shown to be useful for reducing ambiguities and improving the accuracy of speech recognition and translation in many systems (Woszczyna and Waibel, 1994), (Nagata, 1992), (qu et al., 1996).</Paragraph>
    </Section>
    <Section position="2" start_page="0" end_page="16" type="sub_section">
      <SectionTitle>
2.1 Rule-based Approaches
</SectionTitle>
      <Paragraph position="0"> One of the traditional approaches to this area of pragmatics is to recognize speech act types compositionally using syntactic and semantic rules plus  a few pragmatic principles, such as felicity conditions for each speech act type. Spoken language expressions, however, tend to deviate from conventional grammars, and a system consisting of layers of rule-based modules is often too brittle to handle naturally-occurring spoken input. Furthermore, there are a number of fully- or semi-lexicalized morpheme sequences that carry specific illocutionary forces but that are not totally predictable from its forms. These sequences have an institutionalized function in the particular community, and are best accounted holistically rather than analytically (Paw-Icy and Syder, 1983).</Paragraph>
    </Section>
    <Section position="3" start_page="16" end_page="16" type="sub_section">
      <SectionTitle>
2.2 Pattern Matching
</SectionTitle>
      <Paragraph position="0"> Many spoken language systems have thus been using robust pattern-matching techniques to overcome these problems. They use detailed, task-specific templates and semantic grammars, which can recognize various fixed phrases to mark speech act types while skipping over disflucncies in the input. This method has been shown to be successful in many dialoguc systems (Jackson ct al., 1991), (Ward, 1991).</Paragraph>
    </Section>
  </Section>
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