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<Paper uid="A97-2006">
  <Title>An Improvement in the Selection Process of Machine Translation Using Inductive Learning with Genetic Algorithms</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Many studies have been carried out on machine translation and a number of problems has been recognized. Rule-based machine translation (Hutchins and Somers, 1992) could not deal adequately with various linguistic phenomena due to the use of limited rules. To resolve this problem, Example-based machine translation (Sato and Nagao, 1990) has recently been proposed. However, this method requires many translation examples to achieve a practical and high-quality translation.</Paragraph>
    <Paragraph position="1"> Echizen-ya and others previously proposed a method of Machine Translation using Inductive Learning with Genetic Algorithms (GA-ILMT), and this method has been evaluated(Echizen-ya et al., 1996). By applying genetic algorithms, we consider that our proposed method can effectively solve problems that Example-based machine translation would require many translation examples. However, the results of the evaluation experiments show that this method has some problems. The main problem is that many erroneous translation rules are produced and these rules cannot be completely removed from the dictionary. Therefore, we need to improve how to apply genetic algorithms to be able to remove erroneous translation rules. In this paper, we describe an improvement in the selection process of GA-ILMT, and confirm the effectiveness of improvement in the selection process of GA-ILMT.</Paragraph>
  </Section>
class="xml-element"></Paper>