Machine Translation and Post-editing in Foreign Language Teaching and Learning: A Systematic Review
Bibliographic record
Abstract
In recent years, the improvement of machine translation (MT) has facilitated advancements in the new translation mode of Machine Translation Post-editing (MTPE). This study compiles and synthesizes existing literature on post-editing, specifically focusing on text type, evaluation, and pedagogical implication. It is conducted by Systematic Reviews and Meta-Analyses (PRISMA) framework. The findings suggest that the majority of source texts utilized for MTPE in language learning involve in the general domain, with newspapers being the most frequently employed option. Meanwhile, the results indicate that the translation outputs of NMT post-editing and from-scratch translation were comparable when evaluating PE outcomes in terms of accuracy and fluency. Furthermore, the framework proposed by Krings [1] for assessing Post-editing Effort (PEE) has gained significant acceptance in the field. Ultimately, the utilization of NMT can yield benefits and efficacy in the field of language acquisition. The review also suggests future research directions to analyze issues and advance regarding to post-editing.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".