First Steps Towards Dialogue Modelling from an Un-Annotated Human-Human Corpus

Abstract

Virtual human characters equipped with natural language dialogue capability have proved useful in many fields like simulation training and interactive games. Generally behind such dialogue managers lies a complex knowledge-rich rule-based system. Building such system involves meticulous annotation of data and hand autoring of rules. In this paper we build a statistical dialogue model from role play and wizard of oz dialog corpus with virtually no annotation. We compare these methods with the traditional approaches. We have evaluated these systems for perceived appropriateness of response and the results are presented here.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
AD1170964

Entities

People

  • David R Traum
  • Sudeep Gandhe

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Amoebic Dysentery
  • Automated Speech Recognition
  • Case Studies
  • Computational Linguistics
  • Dialogue Systems
  • Human Behavior
  • Information Retrieval
  • Language
  • Linguistics
  • Models
  • Natural Languages
  • Personality
  • Reasoning
  • Rule Based Systems
  • Segmented
  • Simulations
  • Test And Evaluation
  • Training

Readers

  • Artificial Intelligence
  • Computational Linguistics