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<Paper uid="C02-1047">
  <Title>Towards a Noise-Tolerant, Representation-Independent Mechanism for Argument Interpretationa0</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Related Research
</SectionTitle>
    <Paragraph position="0"> Our research integrates reasoning under uncertainty for plan recognition in discourse understanding with the application of the MML principle (Wallace and Boulton, 1968).</Paragraph>
    <Paragraph position="1"> BNs in particular have been used in several such plan recognition tasks, e.g., (Charniak and Goldman, 1993; Horvitz and Paek, 1999; Zukerman, 2001). Charniak and Goldman's system handled complex narratives, using a BN and marker passing for plan recognition. It automatically built and incrementally extended a BN from propositions read  in a story, so that the BN represented hypotheses that became plausible as the story unfolded. In contrast, we use a BN to constrain our understanding of the propositions in an argument, and apply the MML principle to select a plausible interpretation. Both Horvitz and Paek's system and Zukerman's handled short dialogue contributions. Horvitz and Paek used BNs at different levels of an abstraction hierarchy to infer a user's goal in information-seeking interactions with a Bayesian Receptionist. Zukerman used a domain model and user model represented as a BN, together with linguistic and attentional information to infer a user's goal from a short-form rejoinder. However, the combination of these knowledge sources was based on heuristics.</Paragraph>
    <Paragraph position="2"> The MML principle is a model selection technique which applies information-theoretic criteria to trade data fit against model complexity. Selected applications which use MML are listed in http://www.csse.monash.edu.au/a0 dld/ Snob.application.papers.</Paragraph>
  </Section>
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