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<Paper uid="C92-2107">
  <Title>Machine Translation by Case Generalization</Title>
  <Section position="6" start_page="0" end_page="0" type="evalu">
    <SectionTitle>
5 Discussion
</SectionTitle>
    <Paragraph position="0"> In the CBMT approach, the linguistic model, which is a set of translation patterns, is important both for the compaction ratio of a case-base and for similarity metrics. If the model is not appropriate, most cases remains ungeneralized, and unnatural cases are retrieved as similar eases to the inpnt. The problems of constructing linguistic models axe the same as in rule-based systems.</Paragraph>
    <Paragraph position="1"> However, our approach assumes that the linguistic nmdel does not include controls of rules and generalized cases. Whether or not this assumption is correct, it is very ditfieult to define controls in such a way that any exceptional cases axe encapsulated properly. Our approach provides a~l engineering solution to these difficulties. null In our approach, the quality of translations depends on the quantity of cases rather than the quality of the thesaurus. Therefore, it is important to explore (semi-)automatic case acquisition from bilingual corpora. null To construct a huge case-base is easier than to construct a well-defined thesaurus, because cases are construeted locally without taking account of side-effects. To define an effective thesaurus for translation, every effective category for translation must be included, and every intermediate category that is effective for translation must be included in order to calculate semantic distances properly.</Paragraph>
    <Paragraph position="2"> If, on the other hand, thesauri can be developed independently from the case-base, developers or users can select the most appropriate thesaurus for the domain, null</Paragraph>
  </Section>
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