1 1 Statistical Machine Translation Using Monolingual Corpora
Bibliographic record
Abstract
Since the eighties, new approaches in machine translation have been explored. These new approaches are usually based on statistical and pattern matching techniques. Currently, all SMT approaches are based on very large parallel corpora or bitexts. A statistical model is trained on these bitexts and then used to translate new sentences. One big disadvantage for this approach is the fact that for most languages, there are almost no large bitexts available. On the other hand, more and more reasonably large monolingual corpora are becoming available for a wide range of languages. The novelty in the METIS project is the elimination of the use of bitexts altogether. It aims to investigate the possibility of developing a reasonable SMT system without relying on bilingual corpora. Instead, the proposed system will rely on large corpora of monolingual texts, together with some standard linguistic technology and the use of a bilingual lexicon and tag-mapping rules. 1
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.020 |
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".