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<Paper uid="N06-1056">
  <Title>Learning for Semantic Parsing with Statistical Machine Translation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a novel statistical approach to semantic parsing, WASP, for constructing a complete, formal meaning representation of a sentence. A semantic parser is learned given a set of sentences annotated with their correct meaning representations. The main innovation of WASP is its use of state-of-the-art statistical machine translation techniques. A word alignment model is used for lexical acquisition, and the parsing model itself can be seen as a syntax-based translation model.</Paragraph>
    <Paragraph position="1"> We show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods requiringsimilaramountofsupervision,and null shows better robustness to variations in task complexity and word order.</Paragraph>
  </Section>
class="xml-element"></Paper>