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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="6" start_page="2" end_page="2" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In existing Example-Based MT systems based on learning algorithms, similar translationpairs must exist to acquire high-quality translation rules. This means that the systems require large amounts of translation examples to acquire high-quality translation rules. On the other hand, a system with RCL can acquire many new translation rules from sparse translation examples because it uses other already acquired translation rules based on the learning algorithms described in section 2. As a result, the quality of the translation and the effective translation rate of our system is higher than other Rule-Based MT systems. However, our system still does not reach the level of a practical MT system and requires more translation rules to realize the goal of a practical MT system. Although our system is not a practical enough MT system, the system can effectively acquire the translation rules from sparse data by using RCL. Therefore, we consider that the quality of translation improves only by adding new translation examples without the difficulty ofRule-BasedMT systemsin which adeveloper mustcompletelydescribelarge-scaleknowledge.</Paragraph>
    <Paragraph position="1"> In the future, we plan to add a mechanism that effectively combines the acquired translation rules so that the system realizes the translation of practical SL sentences.</Paragraph>
  </Section>
class="xml-element"></Paper>