Translation Wikified: How will Massive Online Collaboration Impact the World of Translation?
Bibliographic record
Abstract
Massively collaborative sites like Wikipedia, YouTube and SecondLife are revolutionizing the way in which content is produced and consumed worldwide. These fundamentally collaborative technologies will have a profound impact on the way in which content is not only produced, but also translated. In this paper, we raise a number of questions that naturally arise in this new frontier of translation. Firstly, we look at what processes and tools might be needed to translate content that is constantly being edited collaboratively by a large, loosely coordinated community of authors. Secondly, we look at how translators might benefit from open, wiki-like translation resources. Thirdly, we look at whether collaborative semantic tagging could help improve Machine Translation by allowing large numbers of people to teach machines facts about the world. These three questions illustrate the various ways in which massive online collaboration might change the rules of the game for translation, by sometimes introducing new problems, sometimes enabling new and better solutions to existing problems, and sometimes introducing exciting new opportunities that simply were not on our minds before.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.024 | 0.044 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".