Like I said, it's debatable. It was honestly the weakest part of the paper. The specific claim comes from a vaguely specified "human evaluation" (i.e. how many evaluators? based on what criterion? did you just ask folks at google to compare stuff?) on a tiny sample of 500 basic sentences, and the results were presented as differences between mean scores without so much as a confidence interval or a power calculation to back them.

Moreover, from the paper:

"In some cases human and GNMT translations are nearly indistinguishable on the relatively simplistic and isolated sentences sampled from Wikipedia and news articles for this experiment. Note that we have observed that human raters, even though fluent in both languages, do not necessarily fully understand each randomly sampled sentence sufficiently and hence cannot necessarily generate the best possible translation or rate a given translation accurately. Also note that, although the scale for the scores goes from 0 (complete nonsense) to 6 (perfect translation) the human translations get an imperfect score of only around 5 in Table 10, which shows possible ambiguities in the translations and also possibly non-calibrated raters and translators with a varying level of proficiency. Testing our GNMT system on particularly difficult translation cases and longer inputs than just single sentences is the subject of future work."

Basically, you should ignore the claims of improvement, and replace them with a big question mark.