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<Paper uid="W06-1501">
  <Title>References</Title>
  <Section position="3" start_page="1" end_page="1" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> This paper is part of a larger investigation into parsing Arabic dialects (Rambow et al., 2005; Chiang et al., 2006). In that investigation, we examined three different approaches: * Sentence transduction, in which a dialect sentence is roughly translated into one or more MSA sentences and then parsed by an MSA parser.</Paragraph>
    <Paragraph position="1"> * Treebank transduction, in which the MSA treebank is transduced into an approximation of a LA treebank, on which a LA parer is then trained.</Paragraph>
    <Paragraph position="2"> * Grammar transduction, which is the name given in the overview papers to the approach discussed in this paper. The present paper provides for the rst time a complete technical presentation of this approach.</Paragraph>
    <Paragraph position="3"> Overall, grammar transduction outperformed the other two approaches.</Paragraph>
    <Paragraph position="4"> In other work, there has been a fair amount of interest in parsing one language using another language, see for example (Smith and Smith, 2004; Hwa et al., 2004). Much of this work, like ours, relies on synchronous grammars (CFGs). However, these approaches rely on parallel corpora. For MSA and its dialects, there are no naturally occurring parallel corpora. It is this fact that has led us to investigate the use of explicit linguistic knowledge to complement machine learning.</Paragraph>
  </Section>
class="xml-element"></Paper>