Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Model for Adapting Explanations to the User‘s Likely Inferences
User Modeling and User-Adapted Interaction
A model for generating better explanations
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Towards the generation of rebuttals in a Bayesian Argumentation System
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
A Probabilistic Approach for Argument Interpretation
User Modeling and User-Adapted Interaction
Modeling suppositions in users' arguments
UM'05 Proceedings of the 10th international conference on User Modeling
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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.