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Scored Systems
| System | Submitter | System Notes | Constraint | Run Notes | BLEU | BLEU-cased | TER | BEER 2.0 | CharactTER |
|---|---|---|---|---|---|---|---|---|---|
| uedin-nmt (Details) | barry University of Edinburgh |
Nematus deep model, layer norm, bpe, ldc synthetic. | yes | Ensemble of 4 l-r, reranked with ensemble of 4 r-l. |
36.3 |
36.3 |
1.334 |
0.351 |
0.601 |
| xmunmt (Details) | Boli Wang Xiamen University |
BPE, CWMT + UN + Xinhua synthetic | yes | ensemble of 4 models |
35.8 |
35.8 |
1.365 |
0.350 |
0.610 |
| test123 (Details) | serenade Peking University |
for test | yes |
35.1 |
35.1 |
0.999 |
0.347 |
0.623 |
|
| toy-nmt (Details) | serenade Peking University |
Single Model | yes |
35.1 |
35.1 |
0.999 |
0.347 |
0.623 |
|
| test123 (Details) | serenade Peking University |
for test | yes |
35.0 |
35.0 |
0.999 |
0.347 |
0.625 |
|
| SogouKnowing-nmt (Details) | Sogou Knowing Sogou |
Ensemble of 5 model, rerank with NMT variants and NgramLM feature, BPE | yes | Ensemble of 5 model, rerank with NMT variants and NgramLM feature, BPE |
34.9 |
34.9 |
1.402 |
0.345 |
0.607 |
| test123 (Details) | serenade Peking University |
for test | yes |
34.9 |
34.8 |
0.999 |
0.346 |
0.606 |
|
| uedin-nmt (Details) | barry University of Edinburgh |
Nematus deep model, layer norm, bpe, ldc synthetic. | yes | Single best |
34.6 |
34.6 |
1.246 |
0.343 |
0.623 |
| test123 (Details) | serenade Peking University |
for test | yes |
34.5 |
34.5 |
failed |
0.345 |
0.612 |
|
| xmunmt (Details) | Boli Wang Xiamen University |
BPE, CWMT + UN + Xinhua synthetic | yes | single model |
34.3 |
34.3 |
1.389 |
0.343 |
0.627 |
| test-sky (Details) | gtang Uppsala University |
yes |
34.0 |
34.0 |
28.968 |
0.182 |
0.781 |
||
| test123 (Details) | serenade Peking University |
for test | yes |
33.5 |
33.5 |
0.998 |
0.340 |
0.622 |
|
| bit_1 (Details) | bit-nmt |
first | yes |
33.1 |
33.1 |
failed |
0.338 |
0.635 |
|
| test123 (Details) | serenade Peking University |
for test | yes |
32.2 |
32.2 |
1.019 |
0.334 |
0.628 |
|
| SogouKnowing-nmt (Details) | Sogou Knowing Sogou |
Single deep NMT model | yes | Single deep NMT model, reranked with NMT variants and other featues, BPE, etc. |
31.7 |
31.7 |
1.859 |
0.330 |
0.652 |
| jingjingz-en-zh (Details) | jingjingz |
no | transformer-23000 |
31.5 |
31.5 |
17.648 |
0.220 |
0.757 |
|
| jhu-nmt (Details) | sding Johns Hopkins University |
Nematus, BPE, CWMT + UN, 4 ensemble, subsample of 2m back-translated data from xmu-monolingual corpus | yes |
31.1 |
31.1 |
failed |
0.328 |
0.659 |
|
| CASICT-DCU_NMT (Details) | peerachet Institute Of Computing Technology, Chinese Academy Of Sciences |
Neural Machine Translation (RNNSearch) Training Data : CWMT+WMT (In cooperation with School of Computing, Dublin City University) | yes | NMT RNNSearch (CWMT+WMT) (Preserve Chinese punctuation) |
30.5 |
30.5 |
0.999 |
0.325 |
0.697 |
| test123 (Details) | serenade Peking University |
for test | yes |
30.4 |
30.4 |
failed |
0.320 |
0.758 |
|
| xmunmt (Details) | Boli Wang Xiamen University |
BPE, CWMT + UN | yes | single model |
30.4 |
30.4 |
2.513 |
0.321 |
0.660 |
| test123 (Details) | serenade Peking University |
for test | yes |
30.3 |
30.3 |
0.999 |
0.324 |
0.664 |
|
| test123 (Details) | serenade Peking University |
for test | yes |
29.8 |
29.8 |
1.000 |
0.321 |
0.660 |
|
| test123 (Details) | serenade Peking University |
for test | yes |
27.8 |
27.8 |
0.999 |
0.310 |
0.693 |
|
| test123 (Details) | serenade Peking University |
for test | yes |
27.2 |
27.2 |
0.999 |
0.306 |
0.710 |
|
| Transformer_UNOnly (Details) | peerachet Institute Of Computing Technology, Chinese Academy Of Sciences |
yes |
27.2 |
27.2 |
1.000 |
0.305 |
0.677 |
||
| NMT359 (Details) | Frank Zhenyi |
yes |
26.9 |
26.8 |
failed |
0.275 |
0.868 |
||
| Oregon State University S (Details) | cosmmb Oregon State University |
two layers NMT, bpe, decoding search | yes |
25.9 |
25.9 |
5.622 |
failed |
failed |
|
| Oregon State University R (Details) | cosmmb Oregon State University |
two layers NMT, bpe, decoding search | yes |
25.5 |
25.5 |
6.757 |
failed |
failed |
|
| UU-HNMT (Details) | gtang Uppsala University |
Helsinki Neural Machine Translation system | yes | HNMT with hybrid encoder and character-level decoder, ensemble of 3 models. |
23.9 |
23.9 |
1.000 |
failed |
failed |
| PROMT SMT (Details) | Alex Molchanov PROMT LLC |
no |
23.5 |
23.5 |
2.292 |
0.285 |
0.788 |
||
| ParFDA (Details) | bicici |
ParFDA Moses Phrase-based SMT system | yes | en-zh (zh segmented with Stanford segmenter, 2.8 million training sentences) |
20.9 |
20.9 |
3.101 |
failed |
failed |
| ParFDA (Details) | bicici |
ParFDA Moses Phrase-based SMT system | yes | en-zh (zh segmented with Stanford segmenter) |
19.6 |
19.6 |
2.888 |
failed |
failed |
| test-mt (Details) | lyonlys None |
no | |||||||
| Testing system (Details) | Myl Pnt ut |
nematus, 3models, NE, bpe | yes | nematus, 3models, NE, bpe |
failed |
failed |
failed |
0.173 |
0.803 |
| test-mt (Details) | lyonlys None |
no |
failed |
failed |
0.000 |
0.820 |
0.000 |
||
| test-mt (Details) | lyonlys None |
no | |||||||
| shannonai (Details) | zhujinyi Peking University |
no |
failed |
failed |
1.232 |
0.104 |
failed |
||
| test-mt (Details) | lyonlys None |
no |