NRC Russian-English Machine Translation System for WMT 2016
Bibliographic record
Abstract
We describe the statistical machine translation system developed at the National Research Council of Canada (NRC) for the Russian-English news translation task of the First Conference on Machine Translation (WMT 2016). Our submission is a phrase-based SMT system that tackles the morphological complexity of Russian through comprehensive use of lemmatization. The core of our lemmatization strategy is to use different views of Russian for different SMT components: word alignment and bilingual neural network language models use lemmas, while sparse features and reordering models use fully inflected forms. Some components, such as the phrase table, use both views of the source. Russian words that remain out-ofvocabulary (OOV) after lemmatization are transliterated into English using a statistical model trained on examples mined from the parallel training corpus. The NRC Russian-English MT system achieved the highest uncased BLEU and the lowest TER scores among the eight participants in WMT 2016.
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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".