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<Paper uid="W06-1319">
  <Title>Balancing Con icting Factors in Argument Interpretation</Title>
  <Section position="3" start_page="0" end_page="134" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The source-channel approach has been often used for word-based language tasks, such as speech recognition and machine translation (Epstein, 1996; Och and Ney, 2002). According to this approach, an addressee receives a noisy channel (language or speech wave), and decodes this channel to derive the source (idea). The selected source is that with the maximum posterior probability.</Paragraph>
    <Paragraph position="1"> In this paper, we apply the source-channel approach to the interpretation of arguments. This approach enables us to cast argument interpretation as a trade-off between con icting factors, viz model complexity against data t, and structure complexity against belief reasonableness. This trade-off is inspired by the Minimum Message Length (MML) Criterion a model selection method that is the basis for several machine learning techniques (Wallace, 2005). According to this trade-off, a more complex model might t the data better, but the plausibility (priors) of the model must be taken into account to avoid over- tting.1 Our argument interpretation mechanism has been implemented in a system called BIAS (Bayesian Interactive Argumentation System).</Paragraph>
    <Paragraph position="2"> BIAS presents to a user a set of facts about the world (evidence), and the user constructs an argument about a particular goal proposition in light of this evidence. BIAS then generates an interpretation of the user's argument, i.e., it tries to understand the argument. When people try to understand an interlocutor's discourse, their interpretation is in terms of their own beliefs and inference patterns. Likewise, our system's interpretations are in terms of its underlying knowledge representation a Bayesian network (BN). The interpretations generated by BIAS include inferences that connect the propositions in a user's argument, suppositions that postulate a user's beliefs that are necessary to make sense of the argument, and explanatory extensions that justify the inferences in the interpretation (and in the argument). BIAS does not generate its own arguments, rather, it integrates these components to make sense of the user's argument.</Paragraph>
    <Paragraph position="3"> In this paper, we rst describe our basic formalism, which is used to calculate the probability of interpretations that include only inferences, and then show how progressive enhancements of this formalism are used for more informative interpretations. null In Section 2, we explain what is an argument interpretation, and describe brie y the interpretation process. Next, we discuss our probabilistic formalism for selecting an interpretation, which is the focus of this paper. In Section 4, we present  the results of our evaluations, followed by a discussion of related work, and concluding remarks.</Paragraph>
  </Section>
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