Context-Based Speech Act Classification in Intelligent Tutoring Systems

  • Borhan Samei
  • Haiying Li
  • Fazel Keshtkar
  • Vasile Rus
  • Arthur C. Graesser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


In intelligent tutoring systems with natural language dialogue, speech act classification, the task of detecting learners’ intentions, informs the system’s response mechanism. In this paper, we propose supervised machine learning models for speech act classification in the context of an online collaborative learning game environment. We explore the role of context (i.e. speech acts of previous utterances) for speech act classification. We compare speech act classification models trained and tested with contextual and non-contextual features (contents of the current utterance). The accuracy of the proposed models is high. A surprising finding is the modest role of context in automatically predicting the speech acts.


speech act machine learning intelligent tutoring systems 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Borhan Samei
    • 1
  • Haiying Li
    • 1
  • Fazel Keshtkar
    • 2
  • Vasile Rus
    • 1
  • Arthur C. Graesser
    • 1
  1. 1.Institute for Intelligent SystemsUniversity of MemphisUSA
  2. 2.Southeast Missouri State UniversityUSA

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