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<Paper uid="P01-1027">
  <Title>Refined Lexicon Models for Statistical Machine Translation using a Maximum Entropy Approach</Title>
  <Section position="9" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have developed refined lexicon models for statistical machine translation by using maximum entropy models. We have been able to obtain a significant better test corpus perplexity and also a slight improvement in translation quality. We believe that by performing a rescoring on translation word graphs we will obtain a more significant improvement in translation quality.</Paragraph>
    <Paragraph position="1"> For the future we plan to investigate more refined feature selection methods in order to make the maximum entropy models smaller and better generalizing. In addition, we want to investigate more syntactic, semantic features and to include features that go beyond sentence boundaries.</Paragraph>
  </Section>
class="xml-element"></Paper>