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Scored Systems
| System | Submitter | System Notes | Constraint | Run Notes | BLEU | BLEU-cased | TER | BEER 2.0 | CharactTER |
|---|---|---|---|---|---|---|---|---|---|
| RWTH Aachen ensemble of Transformer models (Details) | jschamper RWTH Aachen |
Ensemble of 3-strongest Transformer models; contains Edinburgh's back translation from WMT16; contains filtered version of ParaCrawl (18M sentences); contains backtranslation of news.2017.en.shuffled (13M) | yes |
49.9 |
48.4 |
0.381 |
0.695 |
0.369 |
|
| NMT-SMT Hybrid (Details) | fstahlberg University of Cambridge |
MBR-based combination of neural models and SMT | yes | Fix quotes |
49.3 |
48.0 |
0.385 |
0.692 |
0.374 |
| RWTH Aachen Transformer model (single) (Details) | jschamper RWTH Aachen |
trained on 4 GPUs; num-embed: 1024; num-layers: 6; attention-heads: 16; transformer-feed-forward-num-hidden: 4096; transformer-model-size: 1024; 50k joint-BPE; contains Edinburgh's back translation from WMT16; contains filtered version of ParaCrawl (18M sentences); contains backtranslation of news.2017.en.shuffled (13M) | yes |
49.1 |
47.6 |
0.388 |
0.691 |
0.376 |
|
| NTT Transformer-based System (Details) | makoto-mr NTT |
Based on Transformer Big model. Trained with filtered version of CommonCrawl, ParaCrawl and synthetic corpus of newscrawl2017. R2L reranking. | yes |
48.2 |
46.8 |
0.398 |
0.687 |
0.379 |
|
| JHU (Details) | jhu-nmt Johns Hopkins University |
Marian Deep RNN | yes | Contrastive run, fine-tuned to previous test sets (but not R2L reranking) |
46.6 |
45.3 |
0.409 |
0.679 |
0.392 |
| JHU (Details) | jhu-nmt Johns Hopkins University |
Marian Deep RNN | yes | Marian deep model, ensemble of 4 runs using base data (without Paracrawl), and 1 run with partial Paracrawl, re-back-translated news 2016. R2L Reranking. Primary. |
46.5 |
45.3 |
0.406 |
0.680 |
0.391 |
| MLLP-UPV Transformer Ensemble (Details) | mllp MLLP group - Univ. Politècnica de València |
Transformer base model. Trained with 10M filtered sentences, including Paracrawl, and 20M backtranslated sentences from news-2017. Ensemble of 4 models. | yes |
46.4 |
45.1 |
0.408 |
0.681 |
0.387 |
|
| JHU (Details) | jhu-nmt Johns Hopkins University |
Marian Deep RNN | yes | Marian deep model, ensemble of 4 runs using base data (without Paracrawl), and 1 run with partial Paracrawl, re-back-translated news 2016. Not final system yet. |
46.1 |
44.9 |
0.412 |
0.677 |
0.395 |
| MLLP-UPV Transformer Single (Details) | mllp MLLP group - Univ. Politècnica de València |
Transformer base model. Trained with 10M filtered sentences, including Paracrawl, and 20M backtranslated sentences from news-2017. Single model (contrastive). | yes |
45.9 |
44.7 |
0.411 |
0.677 |
0.392 |
|
| Ubiqus-NMT (Details) | vince62s Ubiqus |
OpenNMT Transformer Base system with Rico's back translation from WMT16 Does not include Paracrawl. | yes | Single system. |
45.1 |
44.1 |
0.412 |
0.674 |
0.407 |
| uedin-de-en-3ens-2rerank (Details) | ugermann University of Edinburgh |
3 transformers ensembled, reranked with 3 R2L systems. Trained with a selection of paracrawl. | yes | Fixed run. The first submission had the wrong input (no BPE). |
45.1 |
43.9 |
0.417 |
0.673 |
0.399 |
| LMU-nmt-wmt18-de-en (Details) | Matthias.Huck LMU Munich |
Nematus encoder-decoder NMT, single hidden layer, no r2l reranking | yes |
42.5 |
40.9 |
0.446 |
0.658 |
0.426 |
|
| Fascha (Details) | Fascha University of Regensburg |
yes |
40.2 |
39.0 |
0.465 |
0.646 |
0.464 |
||
| NJUNMT-private (Details) | ZhaoChengqi Nanjing University |
yes | transformer base |
39.6 |
38.3 |
0.479 |
0.644 |
0.450 |
|
| parfda (Details) | bicici |
parfda Moses phrase-based SMT | yes | de-en using PRO for tuning |
34.6 |
33.4 |
0.529 |
0.619 |
0.514 |
| RWTH Unsupervised NMT Ensemble (Details) | yunsukim RWTH Aachen University |
(Unsupervised) Transformer with shared encoder/decoder, separate top-50k word vocabs, iterative back-translation, ensemble 4x | yes |
19.9 |
18.6 |
0.663 |
0.518 |
0.622 |
|
| RWTH Unsupervised NMT Single (Details) | yunsukim RWTH Aachen University |
(Unsupervised) Transformer with shared encoder/decoder, separate top-50k word vocabs, iterative back-translation | yes |
19.5 |
18.1 |
0.669 |
0.513 |
0.648 |
|
| LMU-unsupervised-nmt-wmt18-de-en (Details) | Matthias.Huck LMU Munich |
Unsupervised NMT (no parallel training corpora) | yes |
18.8 |
17.9 |
0.684 |
0.511 |
0.679 |