Paper: A Machine Learning Approach To The Automatic Evaluation Of Machine Translation

ACL ID P01-1020
Title A Machine Learning Approach To The Automatic Evaluation Of Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001
Authors

We present a machine learning approach to evaluating the well- formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. This approach can be used to evaluate an MT system, tracking improvements over time; to aid in the kind of failure analysis that can help guide system development; and to select among alternative output strings. The method presented is fully automated and independent of source language, target language and domain.