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<Paper uid="P01-1027">
  <Title>Refined Lexicon Models for Statistical Machine Translation using a Maximum Entropy Approach</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Typically, the lexicon models used in statistical machine translation systems do not include any kind of linguistic or contextual information, which often leads to problems in performing a correct word sense disambiguation. One way to deal with this problem within the statistical framework is to use maximum entropy methods. In this paper, we present how to use this type of information within a statistical machine translation system. We show that it is possible to significantly decrease training and test corpus perplexity of the translation models. In addition, we perform a rescoring of a2 -Best lists using our maximum entropy model and thereby yield an improvement in translation quality. Experimental results are presented on the so-called &amp;quot;Verbmobil Task&amp;quot;.</Paragraph>
  </Section>
class="xml-element"></Paper>