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When will machine translation achieve a "word accuracy score" comparable to that of professional human translators?
The IBM T. J. Watson Research Center introduced BLEU in 2002 as a method for automatic evaluation of Machine Translation (MT). As formally presented in this paper, for a BLEU MT system, the closer a machine's output is to that of a human, the higher quality and therefore better score. The system is noted for "correlating well with human judgement" and its metrics are widely used as a benchmark for scoring MT.
The current state-of-the-art for English-French MT using the BLEU scoring system is 40.56, set in 2017 by a team lead by Google Brain researchers with their Sparsely-Gated Mixture-of-Experts layer (MoE), a massive neural network made up of thousands of feed-forward sub-networks. It was presented in this paper. Generally, since MT tells us how precisely a machine can translate languages, it is a priori to a multilingual "understanding."
A BLEU score of 50 is comparable to professional human translations, we split the difference between this and the state-of-the-art and ask:
In what year will a English-French MT system achieve a BLEU score of 45 BLEU scoring system?
Resolution is by credible published/posted research paper. The score must be comparable in terms of testing corpus etc. to previous MT scores such as quoted above.
(Edited 8/7/17 to tweak resolution criteria.)
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