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<Paper uid="C04-1005">
  <Title>Improving Statistical Word Alignment with a Rule-Based Machine Translation System</Title>
  <Section position="6" start_page="321" end_page="321" type="concl">
    <SectionTitle>
7 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> This paper proposes an approach to improve statistical word alignment results by using a rule-based translation system. Our contribution is that, given a rule-based translation system that provides appropriate translation candidates for each source word or phrase, we select appropriate alignment links among statistical word alignment results or modify them into new links. Especially, with such a translation system, we can identify both the continuous and separated phrases in the source language and improve the multi-word alignment results. Experimental results indicate that our approach can achieve a precision of 85% and a recall of 71% for word alignment including null links in general domains. This result significantly outperforms those of the methods that use a bilingual dictionary to improve word alignment, and that only use statistical translation models.</Paragraph>
    <Paragraph position="1"> Our future work mainly includes three tasks.</Paragraph>
    <Paragraph position="2"> First, we will further improve multi-word alignment results by using other technologies in natural language processing. For example, we can use named entity recognition and transliteration technologies to improve person name alignment. Second, we will extract translation rules from the improved word alignment results and apply them back to our rule-based machine translation system. Third, we will further analyze the effect of the translation system on the alignment results.</Paragraph>
  </Section>
class="xml-element"></Paper>