NL Generation for Virtual Humans in a Complex Social Environment

Abstract

Natural language generation is a broad field, given the wide variety of different applications for text generation. Perhaps one of the most challenging of these applications is natural language generation for spoken dialogue systems. In spoken dialogue systems, real-time throughput is required, which constrains the processing to less than a second if the system is to seem natural, especially given other processing of input and output. Thus text generation approaches which involve selecting from among many possible alternatives or involve complex calculations to determine preferences (Langkilde& Knight 1998) is not appropriate. Generation in dialogue is also somewhere in between single-shot sentence generation and generation of extended discourse. On the one hand, single short utterances must be generated because one can not predict a priori exactly how the other dialogue participant(s) will react, and subsequent generation may depend more on the input that is newly provided than any previously available information. On the other hand, dialogues generally have a coherent structure, depending on the goals and overall structure of the task that is being discussed as well as the immediately previous utterance (Grosz & Sidner 1986). Thus text-planning notions are still relevant, even if one can not count on being able to produce paragraph-level or longer utterances as pre-planned due to the interactive nature of dialogue.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA459528

Entities

People

  • David R Traum
  • Eduard Hovy
  • Michael Fleischman

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cognitive Science
  • Computational Linguistics
  • Computational Science
  • Computer Science
  • Demographic Cohorts
  • Dialogue Systems
  • Environment
  • Language
  • Linguistics
  • Machine Translation
  • Multiagent Systems
  • Natural Languages
  • Production
  • Recognition
  • Social Environment
  • Virtual Reality

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Linguistics
  • Computational Modeling and Simulation