Balancing conflicting factors in argument interpretation

We present a probabilistic approach for the interpretation of arguments that casts the selection of an interpretation as a model selection task. In selecting the best model, our formalism balances conflicting factors: model complexity against data fit, and structure complexity against belief reasonableness. We first describe our basic formalism, which considers interpretations comprising inferential relations, and then show how our formalism is extended to suppositions that account for the beliefs in an argument, and justifications that account for the inferences in an interpretation. Our evaluations with users show that the interpretations produced by our system are acceptable, and that there is strong support for the postulated suppositions and justifications.