I gave the paper a quick read and there's an impressive amount of context awareness. However one particular thing it seems like it wouldn't handle well would be ideosyncratic speech that is particular to a specific character.
examples:
- a character who uses clunky or outdated forms of Japanese
- a character meant to have a rural account
- a character with a speech affectation, like adding a cutesy -nyo to the end of their words/sentences
- etc
Perhaps these could be improved if the model was trained on human translations for that specific character and weighted appropriately. With some manga running for dozens or hundreds of volumes that would feasible.
It also definitely wouldn't help with puns, cultural references or other subtleties!
BUT, this could still be really useful as an aid to human translators.
examples:
- a character who uses clunky or outdated forms of Japanese
- a character meant to have a rural account
- a character with a speech affectation, like adding a cutesy -nyo to the end of their words/sentences
- etc
Perhaps these could be improved if the model was trained on human translations for that specific character and weighted appropriately. With some manga running for dozens or hundreds of volumes that would feasible.
It also definitely wouldn't help with puns, cultural references or other subtleties!
BUT, this could still be really useful as an aid to human translators.