I'm not entirely sure why low resource languages are seen as such a high priority for AI research. It seems that by definition there's little payoff to solving translation for them.
Some numbers (but you can not exactly infer from them such accumulated numbers): https://en.wikipedia.org/wiki/List_of_languages_by_total_num...
Some more numbers from here: https://www.sciencedirect.com/science/article/pii/S016763931...
"96% of the world’s languages are spoken by only 4% of its people."
Although this statement is more about the tail from the approx 7000 languages.
For instance, Icelandic is not supported by any modern smartphone platform which has lead to Icelandic natives communicating with each other in English and very little information is translated to Icelandic[1,2].
That being said, I am worried that having translations that are "too good" could also act to accelerate language death as the importance of keeping languages alive will seem less significant (to non-language-nerds) if we can translate works written in that language to any other language with very small datasets. Luckily I'm not convinced that AI models will be able to produce convincing and consistent translations for a long time -- languages are so different in so many ways that I can't see how adding more dimensions and parameters to a model would account for them.
[1]: https://youtu.be/qYlmFfsyLMo?t=141 [2]: https://www.nytimes.com/2017/04/22/world/europe/iceland-icel...
The reason for emphasis is in my opinion twofold: 1) Allowing these people to use the fancy language technology in their own language is good in and of itself. 2) Training neural networks on fewer resources is more difficult than using more resources and therefore a fun and interesting challenge.
Furthermore, Facebook considers the internet to consist of Facebook and Wikipedia (Zero).
I view this as just another extension of their Next Billion initiative, an effort to ensure that another billion people are monopolised by Facebook.
That's the payoff.