Research: Two source languages - train and inference off of both
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- Dominant language
- Python
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Description
Spin off from: https://github.com/sillsdev/serval/issues/265
Different source languages for the different drafts.
- Train an xx/yy translation model, then translate book A from the source translation (xx) into the target language.
- Train a zz/yy translation model, then translate book A from the source translation (zz) into the target language.
Can we train one model to translate from both sources to one target? That is what NLLB does - how can we do it best? Would it be better just to train 2 models in Serval and switch between them?
This could also ask the question about multiple source texts from the same language, possibly with tags for different translations, etc. It is part of a broader discussion about multiple sources and potentially multiple drafts for the target for a given verse.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked silnlp issue #265 and the NLLB approach referenced in this issue. Compare a shared model for two source languages with training two separate models, including the question of multiple source texts or translation tags. Done means a documented design decision with enough scope to guide implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100