MRE: A Study on Evolutionary Language Understanding

Abstract

The lack of well-annotated data is always one of the biggest problems for most training-based dialogue systems. Without enough training data, it's almost impossible for a trainable system to work. In this paper, we explore the evolutionary language understanding approach to build a natural language understanding machine in a virtual human training project. We build the initial training data with a finite state machine. The language understanding system is trained based on the automated data first and is improved as more and more real data come in, which is proved by the experimental results.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2015
Accession Number
AD1171347

Entities

People

  • Donghui Feng
  • Eduard Hovy

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Automated Speech Recognition
  • Autonomous Agents
  • Classification
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Context Free Grammars
  • Data Sets
  • Dialogue Systems
  • Grammars
  • Information Science
  • Language
  • Linguistics
  • Machine Learning
  • Machine Translation
  • Natural Language Processing
  • Natural Language Understanding
  • Natural Languages
  • Precision
  • Probability
  • Training
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Instructional Design and Training Evaluation.