STRATEGIES TO DEFEND A PROTAGONIST OF AN EVENT
We aim at controlling the biases that exist in every description, in order to give the best possible image of one of the protagonists of an event. Starting from a supposedly complete set of propositions accounting for an event, we develop various argumentative strategies (insinuation, justification, reference to customary norms) to imply the facts that cannot be simply omitted but have the "wrong" orientation w.r.t. the protagonist we defend. By analyzing these different strategies, a contribution of this work is to provide a number of relevant parameters to take into account in developing and evaluating systems aiming at understanding natural language (NL) argumentations. The source of inspiration for this work is a corpus of 160 texts where each text describes a (different) car accident. Its result, for a given accident, is a set of first-order literals representing the essential facts of a description intended to defend one of the protagonists. An implementation in Answer Set Programming is underway. A couple of examples showing how to extract, from the same starting point, a defense for the two opposite sides are provided. Experimental validation of this work is in progress, and its first results are reported.
strategies argumentation natural language inference rules nonmonotonic reasoning semi-normal defaults