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I saw an ELI5 on stylesynthesis and it got me wondering about how it actually works when dealing with different languages. Is it as simple as just training the model on multilingual datasets, or are there more intricate techniques involved like using separate encoders/decoders for different languages, or some kind of language-agnostic feature representation? If it's the first, wouldn't the quality suffer compared to a model trained only on one language due to the expanded vocabulary space?
Thinking about style transfer specifically (the part that gets me), it seems like transferring a "professional" tone written in English to a casual translation in Spanish could be tricky. Simply swapping words wouldn't capture the essence stylistic nuances. Do these models rely on parallel corpora to learn mappings between styles in different languages, or is there a more clever solution that doesn't depend on having large amounts of aligned data? It feels like accurately capturing both the content and the style across languages is a really arduous problem to solve well.