Improving the Presentation of Argument Interpretations Based on User Trials

The interpretation of complex discourse, such as arguments, is a difficult task that often requires validation, i.e., a system may need to present its interpretation of a user’s discourse for confirmation. In this paper, we consider the presentation of discourse interpretations in the context of a probabilistic argumentation system. We first describe our initial approach to the presentation of an interpretation of a user’s argument; this interpretation takes the form of a Bayesian subnet. We then report on the results of our preliminary evaluation with users, focusing on their criticisms of our system’s output. These criticisms motivate a content enhancement procedure that adds information to explain unexpected outcomes and removes superfluous content from an interpretation. The discourse generated by this procedure was found to be more acceptable than the discourse generated by our original method.