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In this paper, we present a method of improving the accuracy of machine translation evaluation of Czech sentences. Given a reference sentence, our algorithm transforms it by targeted paraphrasing into a new synthetic reference sentence that is closer in wording to the machine translation output, but at the same time preserves the meaning of the original(More)
We present a method for improving machine translation (MT) evaluation by targeted paraphrasing of reference sentences. For this purpose, we employ MT systems themselves and adapt them for translating within a single language. We describe this attempt on two types of MT systems – phrase-based and rule-based. Initially, we experiment with the freely available(More)
In this paper, we present a method of improving quality of machine translation (MT) evaluation of Czech sentences via targeted paraphrasing of reference sentences on a deep syntactic layer. For this purpose, we employ NLP framework Treex and extend it with modules for targeted paraphrasing and word order changes. Automatic scores computed using these(More)
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