Name
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. |
Year | Venue | Keywords |
|---|---|---|
2010 | AGS | semi-automated dialogue act classification,dynamic dialogue,gameplay log,statistical model,social agent,speech act,interleaved action,restaurant game,semiautomatically classified dialogue act,interleaved utterance,classified utterance,dialogue act triple,natural language,natural language processing,virtual environment,social simulation,artificial intelligent,social behavior |
Field | DocType | Citations |
Situated,Predictive power,Computer science,Dialogue acts,Referent,Social simulation,Natural language processing,Social agents,Statistical model,Artificial intelligence,Classifier (linguistics) | Conference | 6 |
PageRank | References | Authors |
0.63 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeff Orkin | 1 | 173 | 14.29 |
| Deb Roy | 2 | 1033 | 92.10 |