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<Paper uid="C02-1158">
  <Title>Study of Practical Effectiveness for Machine Translation Using Recursive Chain-link-type Learning</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> A number of machine translation systemsbased on thelearningalgorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text corpora. This means that it is difficult for the systems to acquire the translation rules from sparse data. As a result, these methods require large amounts of training data in order to acquire high-quality translation rules. To overcomethis problem, we propose a method ofmachine translation using a Recursive Chain-link-type Learning. In our new method, the system can acquire many new high-quality translation rules from sparse translation examples based on already acquired translation rules. Therefore, acquisition of new translation rules results in the generation of more new translation rules.</Paragraph>
    <Paragraph position="1"> Such aprocessofacquisitionoftranslationrules islikealinkedchain. Fromtheresultsofevaluationexperiments,weconfirmedtheeffectiveness null of Recursive Chain-link-type Learning.</Paragraph>
  </Section>
class="xml-element"></Paper>