Google's massive language translation work identifies where it goofs up

Scores for languages when translating from English and back to English again, correlated to how many sample sentences the language has. Toward the right side, higher numbers of example sentences result in better scores. There are outliers, such as English in Cyrillic, which has very few examples but translates well. 

Bapna et al., 2022

What do you do after you have collected writing samples for a thousand languages for the purpose of translation, and humans still rate the resulting translations a fail?

Examine the failures, obviously. 

And that is the interesting work that Google machine learning scientists related this month in a massive research paper on multi-lingual translation, "Building Machine Translation Systems for the Next Thousand Languages," posted on Arxiv.

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