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<Paper uid="W02-0227">
  <Title>A Minimum Message Length Approach for Argument Interpretation</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Related Research
</SectionTitle>
    <Paragraph position="0"> Our research integrates plan recognition for discourse understanding with the application of the MML principle (Wallace and Boulton, 1968).</Paragraph>
    <Paragraph position="1"> The system described in (Carberry and Lambert, 1999) recognized a user's intentions during expertconsultation dialogues. This system considered several knowledge sources for discourse understanding.</Paragraph>
    <Paragraph position="2"> It used plan libraries as its main knowledge representation formalism, and handled short conversational turns. In contrast, our system relies on BNs and handles unrestricted arguments.</Paragraph>
    <Paragraph position="3"> BNs have been used in several systems that perform plan recognition for discourse understanding, 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. Marker passing was used to restrict the nodes included in the BN. In contrast, we use domain knowledge to constrain our understanding of the propositions in a user's argument, and apply the MML principle to select a plausible interpretation.</Paragraph>
    <Paragraph position="4"> Like Carberry and Lambert's system, 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. In addition, they used decision-theoretic strategies to guide the progress of the dialogue. We expect to use such strategies when our system engages in a full dialogue with the user. In previous work, 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="5"> The approach presented in this paper extends our previous work in that (1) it handles input of unrestricted length, (2) it offers a principled technique for selecting between alternative interpretations of a user's discourse, and (3) it handles discrepancies between the user's input and the system's expectations at all levels (wording, beliefs and inferences). Further, this approach makes no assumptions regarding the synchronization between the user's beliefs and the system's beliefs (but it assumes that the system is a domain expert). Finally, this approach may be extended to incorporate various aspects of discourse and dialogue, such as information pertaining to the dialogue history and user modeling information.</Paragraph>
    <Paragraph position="6"> The MML principle is a model-selection technique which applies information-theoretic criteria to trade data fit against model complexity (a glossary of model-selection techniques appears in http://www-white.media.mit.edu/ a0 tpminka/statlearn/glossary). MML has been used in a variety of applications, e.g., in NL it was used for lexical selection in speech understanding (Thomas et al., 1997). In this paper, we demonstrate its applicability to a higher-level NL task.</Paragraph>
  </Section>
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