Machine translation in hindsight
Bibliographic record
Abstract
The paper expands on the analysis of key projects adoring the machine translation (MT) hall of fame and their role in addressing practical tasks. The most successful initiatives suggest that the fledgling MT was contingent on the level of entropy, a.k.a. random nature of natural languages: the lower the indicator, the higher the predictability of the text, and by implication the efficiency of the system. It accounts for the success of the first Georgetown-IBM experiment and Canada’s METEO-1. The letter grew into a full-fledged system that for almost a quarter of the 20th century, provided English-French-English translations of weather bulletins, boasting high language predictability. Although, in between them the 1964 ALPAC report sowed a seed of doubt of the MT validity, it never aimed at killing the research area at all. On the contrary, it highlighted technics and applications, where the technology had demonstrated promising results, including raw MT, post-edited MT, and M-AT. The authors note a cyclic nature of the development of MT-powered methods and technologies. Today’s combination of resources and the way they are used are different very little from those employed in the past century. What makes them stand apart is the maturity of MT technologies, which made it through rulebased, direct, corpus-based, and knowledge-based translation to SMT and eventually to NMT. It has been established that the improved performance comes at a cost of more elaborate and larger data sets, tagged, marked up and annotated for automated use in language models. Taking advantage of these as well as artificial intelligence (AI), the authors venture into modeling basic text processing scenarios in a bilingual environment. This results in recommendations as to future paths for the improvement of MT technologies in the hands of professional translators by fine-tuning language models individually and pursuing post-editing (PEMT) and pre-editing practices paving the way for more complex transformations and lower equivalence levels.
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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.035 |
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".