An Information-theoretic Approach for Argument Interpretation

We describe an information-theoretic argument-interpretation mechanism embedded in an interactive system. Our mechanism receives as input an argument entered through a web interface. It generates candidate interpretations in terms of its underlying knowledge representation -- a Bayesian network, and applies the Minimum Message Length principle to select the best candidate. The results of our preliminary evaluations are encouraging, with the system generally producing plausible interpretations of users' arguments