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<Paper uid="W04-3228">
  <Title>Dependencies vs. Constituents for Tree-Based Alignment</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Given a parallel parsed corpus, statistical tree-to-tree alignment attempts to match nodes in the syntactic trees for a given sentence in two languages. We train a probabilistic tree transduction model on a large automatically parsed Chinese-English corpus, and evaluate results against human-annotated word level alignments. We find that a constituent-based model performs better than a similar probability model trained on the same trees converted to a dependency representation.</Paragraph>
  </Section>
class="xml-element"></Paper>