Toward a scoring function for quality-driven machine translation

Abstract

We describe how we constructed an automatic scoring function for machine translation quality; this function makes use of arbitrarily many pieces of natural language processing software that has been designed to process English language text. By machine-learning values of functions available inside the software and by constructing functions that yield values based upon the software output, we are able to achieve preliminary, positive results in machine-learning the difference between human-produced English and machine-translation English. We suggest how the scoring function may be used for MT system development.