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Virtual instructors can be used in several applications, ranging from trainers in simulated worlds to non player characters for virtual games. In this paper we present a novel algorithm for rapidly prototyping virtual instructors from human-human corpora without manual annotation. Automatically prototyping full-fledged dialogue systems from corpora is far from being a reality nowadays. | Prototyping virtual instructors from human-human corpora Luciana Benotti PLN Group FAMAF National University of Cordoba Cordoba Argentina luciana.benotti@gmail.com Alexandre Denis TALARIS team LORIA CNRS Lorraine. Campus scientifique BP 239 Vandoeuvre-les-Nancy France alexandre.denis@loria.fr Abstract Virtual instructors can be used in several applications ranging from trainers in simulated worlds to non player characters for virtual games. In this paper we present a novel algorithm for rapidly prototyping virtual instructors from human-human corpora without manual annotation. Automatically prototyping full-fledged dialogue systems from corpora is far from being a reality nowadays. Our algorithm is restricted in that only the virtual instructor can perform speech acts while the user responses are limited to physical actions in the virtual world. We evaluate a virtual instructor generated using this algorithm with human users. We compare our results both with human instructors and rule-based virtual instructors hand-coded for the same task. 1 Introduction Virtual human characters constitute a promising contribution to many fields including simulation training and interactive games Kenny et al. 2007 Jan et al. 2009 . The ability to communicate using natural language is important for believable and effective virtual humans. Such ability has to be good enough to engage the trainee or the gamer in the activity. Nowadays most conversational systems operate on a dialogue-act level and require extensive annotation efforts in order to be fit for their task Rieser and Lemon 2010 . Semantic annotation and rule authoring have long been known as bottlenecks for developing conversational systems for new domains. In this paper we present novel a algorithm for generating virtual instructors from automatically an 62 notated human-human corpora. Our algorithm when given a task-based corpus situated in a virtual world generates an instructor that robustly helps a user achieve a given
