  Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation
technique for statistical machine translation to make it more adjustable and
robust. We intend to adapt it to resemble human evaluation more. We perform
experiments to evaluate the performance of our technique against the primary
existing evaluation methods. We describe and show the improvements it makes
over existing methods as well as correlation to them. When human translators
translate a text, they often use synonyms, different word orders or style, and
other similar variations. We propose an SMT evaluation technique that enhances
the BLEU metric to consider variations such as those.
