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<Paper uid="I05-5003">
  <Title>Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence</Title>
  <Section position="7" start_page="22" end_page="23" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have shown that it is possible to derive features that can be used to determine whether similar sentences are paraphrases of each other from methods currently being used to automatically evaluate machine translation systems. The experiments also show that using features that encode the distribution over the POS tag set of both matching words and non-matching words can significantly enhance the performance of a PER-based system on this task.</Paragraph>
    <Paragraph position="1">  This research begs the important question &amp;quot;Is there any correlation between performance on the semantic equivalence classification task and performance of the underlying evaluation technique on the task of MT evaluation?&amp;quot;. Intuitively at least, there certainly should be. If there is, it may bepossibletousethetaskofclassifyingsentences for semantic equivalence as a proxy for the complex and time-consuming task of evaluating evaluation schemes by correlating automatic scores with human scores during the development process of MT evaluation techniques. In future work we look forward to addressing this question, as well as incorporating new features into the models to increase their potency.</Paragraph>
  </Section>
class="xml-element"></Paper>