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<Paper uid="P03-1041">
  <Title>Effective Phrase Translation Extraction from Alignment Models</Title>
  <Section position="10" start_page="0" end_page="0" type="concl">
    <SectionTitle>
9 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented a method to efficiently extract phrase relationships from IBM word alignment models by leveraging the maximum approximation as well as the word lexicon. Our method is significantly less computationally expensive than methods that attempt to explicitly model phrase level interactions within alignment models, and recovers well from noisy alignments at the sentence and corpus level. The significant improvements above the base-line carry through when this method is combined with other phrasal and word level methods. Further experimentation is required to fully appreciate the robustness of this technique, especially when considering a comparable, but not parallel, corpus. The language specific scoring methods have a significant impact on translation quality, and further work to extend these methods to represent specific characteristics of each language, promises to deliver further improvements. Although the method performs well, it lacks an explanatory framework through the extraction process; instead it leverages the well understood fundamentals of the traditional IBM models.</Paragraph>
    <Paragraph position="1"> Combining phrase level knowledge sources within a decoder in an effective manner is currently our primary research interest, specifically integrating knowledge sources of varying reliability. Our method has shown to be an effective contributing component within the translation framework and we expect to continue to improve the state of the art within machine translation by improving phrasal extraction and integration.</Paragraph>
  </Section>
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