Abstract
As a step toward simulating dynamic dialogue between agents and humans in virtual environments, we describe learning a model of social behavior composed of interleaved utterances and physical actions. In our model, utterances are abstracted as {speech act, propositional content, referent} triples. After training a classifier on 100 gameplay logs from The Restaurant Game annotated with dialogue act triples, we have automatically classified utterances in an additional 5,000 logs. A quantitative evaluation of statistical models learned from the gameplay logs demonstrates that semi-automatically classified dialogue acts yield significantly more predictive power than automatically clustered utterances, and serve as a better common currency for modeling interleaved actions and utterances.
Categories and Subject Descriptors
I.2.7 [Artificial Intelligence]: Natural Language Processing – language parsing and understanding.
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Orkin, J., Roy, D. (2011). Semi-Automated Dialogue Act Classification for Situated Social Agents in Games. In: Dignum, F. (eds) Agents for Games and Simulations II. AGS 2010. Lecture Notes in Computer Science(), vol 6525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18181-8_11
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DOI: https://doi.org/10.1007/978-3-642-18181-8_11
Publisher Name: Springer, Berlin, Heidelberg
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