Transferring markup tags in statistical machine translation: a two-stream approach.
Bibliographic record
Abstract
Translation agencies are introducing sta- tistical machine translation (SMT) into the work flow of human translators. Typ- ically, SMT produces a first-draft transla- tion, which is then post-edited by a per- son. SMT has met much resistance from translators, partly because of professional conservatism, but partly because the SMT community has often neglected some practical aspects of translation. Our paper discusses one of these: transferring formatting tags such as bold or italic from the source to the target document with a low error rate, thus freeing the post-editor from having to reformat SMT-generated text. In our “two-stream” approach, tags are stripped from the input to the decoder, then reinserted into the resulting target-language text. Tag trans- fer has been tackled by other SMT teams, but only a few have published descrip- tions of their work. This paper contrib- utes to understanding tag transfer by ex- plaining our approach in detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".