A Machine Learning Approach to the Automatic Evaluation of Machine Translation

We present a machine learning approach to evaluating the wellformedness 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.

• Type: Conference
• Year: 2001
• Where: ACL
• Authors: Simon Corston-Oliver, Michael Gamon, Chris Brockett
• Keywords: ACL 2001, ACL 2007, Machine Learning Approach, Machine Translation System, Machine Translations