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For machine translation purposes I use bleu score, which seems to be the validation mechanism of choice (used in the sutskever 2014 sequence-to-sequence).

The purpose is to get as high bleu as possible (between 0 to 1).

The following mumble gives an extraordinary high bleu score (0.77):

from nltk import bleu_score

reference = ['The moon is very bright']
hypothesis = ['Dee dd ss eee']
reference = [[r.split()] for r in reference]
hypothesis = [[h.split()] for h in hypothesis]

bleu_score.corpus_bleu(reference, hypothesis)

Why does the bleu score give such high accuracy for mumbling? Which other tools for validation could I use for machine translation?

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Which other tools for validation could I use for machine translation?

NLTK's BLEU implementation has a few issues. One of the most commonly used BLEU implementation is the one provided with MOSES (documentation).

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