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<Paper uid="P06-2117">
  <Title>Boosting Statistical Word Alignment Using Labeled and Unlabeled Data</Title>
  <Section position="7" start_page="918" end_page="919" type="concl">
    <SectionTitle>
6 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> This paper proposed a semi-supervised boosting algorithm to improve statistical word alignment with limited labeled data and large amounts of unlabeled data. In this algorithm, we built an interpolated model by using both the labeled data  and the unlabeled data. This interpolated model was employed as a learner in the algorithm. Then, we automatically built a pseudo reference for the unlabeled data, and calculated the error rate of each word aligner with the labeled data. Based on this algorithm, we investigated two methods for word alignment. In addition, we developed a method to combine the results of the above two semi-supervised boosting methods.</Paragraph>
    <Paragraph position="1"> Experimental results indicate that our semi-supervised boosting method outperforms the unsupervised boosting method as described in Wu and Wang (2005), achieving a relative error rate reduction of 19.52%. And it also outperforms the supervised boosting method that only uses the labeled data, achieving a relative error rate reduction of 28.29%. Experimental results also show that all boosting methods outperform their corresponding methods without boosting.</Paragraph>
    <Paragraph position="2"> In the future, we will evaluate our method with an available standard testing set. And we will also evaluate the word alignment results in a machine translation system, to examine whether lower word alignment error rate will result in higher translation accuracy.</Paragraph>
  </Section>
class="xml-element"></Paper>