Semi-automated dialogue act classification for situated social agents in games

  • Authors:
  • Jeff Orkin;Deb Roy

  • Affiliations:
  • MIT Media Laboratory, Cambridge, MA;MIT Media Laboratory, Cambridge, MA

  • Venue:
  • Agents for games and simulations II
  • Year:
  • 2011

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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 semiautomatically 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.