Inferences, suppositions and explanatory extensions in argument interpretation

Abstract

We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user's argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation-a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.

Modeling Cultural Factors in Collaboration and Negotiation

“…Schemas are necessary for identifying arguments, finding missing premises, analyzing arguments, and evaluating arguments (Cohen, 1987;Dung, 1995;Pollock, 1995;Katzav and Reed, 2004;Walton et al, 2008). Furthermore, we looked into work on using machine learning techniques for automatically interpreting (George et al, 2007), generating (Zukerman, 2001), and detecting arguments (Mochales and Moens, 2009).…”

Layered evaluation of interactive adaptive systems: framework and formative methods

“…Importantly, an indirect experiment also ensures that the input to a layer is perfect, making it very suitable for layered evaluations. George et al (2007) used an indirect experiment because they wanted to focus on a particular behaviour of the system that did not always occur and wanted to remove extraneous factors from the evaluation. Indirect user tests are less natural for participants, and the results may therefore be less reliable.…”