Good Applications for Crummy Machine Translation

Abstract

Ideally, we might hope to improve the performance of our MT systems by improving the system, but it might be even more important to improve performance by looking for a more appropriate application. A survey of the literature on evaluation of MT systems seems to suggest that the success of the evaluation often depends very strongly on the selection of an appropriate application. If the application is well-chosen, then it often becomes fairly clear how the system should be evaluated. Moreover, the evaluation is likely to make the system look good. Conversely, if the application is not clearly identified (or worse, if the application is poorly chosen), then it is often very difficult to find a satisfying evaluation paradigm. We begin our discussion with a brief review of some evaluation metrics that have been tried in the past and conclude that it is difficult to identify a satisfying evaluation paradigm that will make sense over all possible applications. It is probably wise to identify the application first, and then we will be in a much better position to address evaluation questions. The discussion will then turn to the main point, an essay on how to pick a good niche application for state-of-the-art (crummy) machine translation. Machine translation, Applications of MT, MT Workstations and evaluations.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1993
Accession Number
ADA278689

Entities

People

  • Eduard H. Hovy
  • Kenneth W. Church

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Applied Computer Science
  • Artificial Intelligence
  • Automated Speech Recognition
  • Computational Linguistics
  • Computer Science
  • Computers
  • Errors
  • European Communities
  • Information Science
  • Language
  • Language Translation
  • Linguistics
  • Machine Translation
  • Natural Language Processing
  • Recognition
  • Translations
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Educational Psychology
  • Software Engineering.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation
  • AI & ML - Neural Networks