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One of my worries about current trends in AI research is the reinforcement of the long-term cultural & linguistic dominance of the Anglosphere. It's not just that English is the exclusive language of research (which seems inevitable). NLP research and models focus on English.
In particular, the lack of pre-trained models for other languages than English is cutting off these languages and cultures from the next generation of AI applications. Languages like Mandarin will probably be fine, but those with small numbers of speakers won't be.
When I worked in vision problems, I never had to worry about such problems. As I take up new projects in language/speech, this is a constant concern. I also feel #NLP research and development should be localized a lot more.
Given the amount of chinese research content on github wonder if we'll see a hard split in the community at some point, especially given then political climate.
But Chinese researchers all learn English. Very few western/foreign researchers are even interested in Chinese language data.
Theoretically, could we just use the same models developed for English and train them with other languages data?
The same model structure is almost always applicable to any languages. The problem here is more about lack of data in other languages.
Knowing what they've done w their facial recognition tech, this knowledge (of China doing NLP research on their minor languages) only brings more worry to me.
Hi, linguists (computational and others) have been saying it for years, @emilymbender being the best known here, check out her work and disseminate please :-)
Thanks @EvpokPadding
@fchollet: You can find a recent summary here:https://thegradient.pub/the-benderrule-on-naming-the-languages-we-study-and-why-it-matters/ …
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