I've learned two languages with the help of MT. Any information and insights about that MT usage that you can share with world-just as you have generously shared your NLLB models-would be most welcome. Like Google, Microsoft, Baidu, DeepL, and others, Meta must have huge amounts of data on how your users are using MT to communicate. We are now struggling to adapt those materials for the age of MT, because data on the actual use of MT is very hard to get. Most education in modern languages is focused on giving students language skills that they will be able to use later in work, education, and daily life, and textbooks and other learning materials are typically shaped around real-world situations. One of the hurdles educators and researchers face is finding out how MT is being used in the real world. We don’t know yet, though, how that use of MT might affect our students’ acquisition of other languages or their motivation to keep studying those languages. The ready availability of MT today, including on Facebook and Instagram, means that language students use it a lot while studying. As I have written in a few papers and essays, advances in MT have raised serious questions for language teachers.
I am a former professional translator (Japanese to English) and am now supervising research at the University of Tokyo on the use of machine translation in second-language education. Thank you for your exciting work and for coming onto HN to respond to questions.
It kinda feels like it runs the risk of passively encouraging people to trust the machine's choice over their own, as long as it isn't obviously nonsense, and the cumulative effect could be rather large after a while. but people are pretty darn good at choosing the easy option of trusting whatever they're given rather than being as careful as they should be. There's some info on it in a blog post and the MediaWiki "Content translation" page, but does anyone know of any studies on the quality of the translations produced? I can absolutely see it being a huge time-saver for people who are essentially fluent in both (there's a lot of semi-mechanical drudgery in translating stuff like this that could be mostly eliminated). While there is very definitely utility in doing things like this, I do kinda fear "poisoning the well"-like effects of feeding (even partially-) AI-generated-data into extremely common AI-data-sources.
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