The Impact of Sentence Alignment Errors on Phrase-Based Machine Translation Performance
Bibliographic record
Abstract
When parallel or comparable corpora are har-vested from the web, there is typically a trade-off between the size and quality of the data. In order to improve quality, corpus collection ef-forts often attempt to fix or remove misaligned sentence pairs. But, at the same time, Statis-tical Machine Translation (SMT) systems are widely assumed to be relatively robust to sen-tence alignment errors. However, there is little empirical evidence to support and character-ize this robustness. This contribution investi-gates the impact of sentence alignment errors on a typical phrase-based SMT system. We confirm that SMT systems are highly tolerant to noise, and that performance only degrades seriously at very high noise levels. Our find-ings suggest that when collecting larger, noisy parallel data for training phrase-based SMT, cleaning up by trying to detect and remove in-correct alignments can actually degrade per-formance. Although fixing errors, when ap-plicable, is a preferable strategy to removal, its benefits only become apparent for fairly high misalignment rates. We provide several expla-nations to support these findings. 1
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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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".