Automatic tools for machine translation (MT) evaluation such as BLEU are well established, but have the drawbacks that they do not perform well at the sentence level and that they presuppose manually translated reference texts. Assuming that the MT system to be evaluated can deal with both directions of a language pair, in this research we suggest to conduct automatic MT evaluation by determining the orthographic similarity between a back-translation and the original source text. This way we eliminate the need for human translated reference texts. By correlating BLEU and back-translation scores with human judgments, it could be shown that the back-translation score gives an improved performance at the sentence level.
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