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<Paper uid="N03-1017">
  <Title>Statistical Phrase-Based Translation</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We created a framework (translation model and decoder) that enables us to evaluate and compare various phrase translation methods. Our results show that phrase translation gives better performance than traditional word-based methods. We obtain the best results even with small phrases of up to three words. Lexical weighting of phrase translation helps.</Paragraph>
    <Paragraph position="1"> Straight-forward syntactic models that map constituents into constituents fail to account for important phrase alignments. As a consequence, straight-forward syntax-based mappings do not lead to better translations than unmotivated phrase mappings. This is a challenge for syntactic translation models.</Paragraph>
    <Paragraph position="2"> It matters how phrases are extracted. The results suggest that choosing the right alignment heuristic is more important than which model is used to create the initial word alignments.</Paragraph>
  </Section>
class="xml-element"></Paper>