Automated aligned translation candidates
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 39
- Forks
- 7
- Avg merge
- 1d 9h
- Merged PRs (30d)
- 5
Description
I've just been working with a translation that got pretty low alignment scores on the source text. I researched the language a bit and managed to find related languages that performed better.
This made me wonder whether it's not worth building an alignment "index" so we can identify clusters, run alignments against samples in each cluster and find decent candidates automatically. Have you guys solved this problem in some other way or done something like this?
Contributor guide
No contributing guide indexed for this repository
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
No file, test, or entry point is named. Start by locating the existing alignment workflow and how alignment scores are produced, then determine whether related languages and sample-based comparisons are already represented. The issue does not define the clustering method, candidate criteria, or completion checks, so those requirements need clarification before 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