The idea of translating between languages by sounds is really stretching my mind.
In any case, text seems to stil form a part:
> During training, we use monolingual speech-text datasets
So there's still a way till machines learn language as humans do, i.e. with sounds as primary modality. But nowadays I won't bet as to how long any ml task for language will take to be solved
1) they use a single, shared embedding space for the two languages, forcing the model to learn "semantics" independently (or rather, interdependently) of language 2) using back-translation for training. I'm not sure that I got this right, but this seems to be round-trip translation? So the model can self-assess its performance by checking the spanish->english->spanish difference.
Sounds very promising and interesting! However, it seems they only tested on spanish and english. I wonder if the similarity of the languages at the lexical level made these results possible.
Aside from the lack of training data in many languages, I get the impression that tech companies like Google have been anglocentric in their approach, resulting in ok results only if at least one of the languages are “big”. That’s one thing that’s amazing about ChatGPT, it doesn’t discriminate between languages much, or, at least it seems like it’s able to transfer knowledge really well between languages. It seems it finds the higher level patterns of human knowledge to the point where language or even style is basically just a frontend.
Ironically, it seems the less you bother to teach computers about linguistics, the better they perform at language.
I really hope LLMs become mainstream in non-tech fields soon. I would love to see its impacts on the social sciences, linguistics, diplomacy (and yes, animal communications!), etc.