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WAT

The Workshop on Asian Translation
Evaluation Results

[EVALUATION RESULTS TOP] | [BLEU] | [RIBES] | [AMFM] | [HUMAN (WAT2019)] | [HUMAN (WAT2018)] | [HUMAN (WAT2017)] | [HUMAN (WAT2016)] | [HUMAN (WAT2015)] | [HUMAN (WAT2014)] | [EVALUATION RESULTS USAGE POLICY]

BLEU


# Team Task Date/Time DataID BLEU
Method
Other
Resources
System
Description
juman kytea mecab moses-
tokenizer
stanford-
segmenter-
ctb
stanford-
segmenter-
pku
indic-
tokenizer
unuse myseg kmseg
1KNU_HyundaiJPCja-zh2019/07/27 08:37:113162-43.19--44.5643.86----NMTYesTransformer(base) + *Used ASPEC corpus* with relative position, bt, r2l rerank, 4-model ensemble(9-check point)
2sarahJPCja-zh2019/07/26 11:39:522982-42.13--43.2742.98----NMTNoTransformer, ensemble of 4 models
3ryanJPCja-zh2019/07/25 22:04:252950-41.22--42.3741.99----NMTNoBase Transformer
4USTCJPCja-zh2018/08/31 16:54:462202-39.71--40.5440.05-- 0.00 0.00NMTNotensor2tensor, 4 model average, r2l rerank
5ORGANIZERJPCja-zh2018/08/15 18:14:011960-39.07--40.3239.75-- 0.00 0.00NMTNoNMT with Attention
6NICT-2JPCja-zh2016/08/05 18:13:161106-33.40--34.6433.83- 0.00 0.00 0.00SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC)
7NICT-2JPCja-zh2016/08/04 17:37:251081-33.35--34.6433.81- 0.00 0.00 0.00SMTNoPhrase-based SMT with Preordering + Domain Adaptation
8ORGANIZERJPCja-zh2016/11/16 11:16:251340-33.04--33.9233.34- 0.00 0.00 0.00NMTYesOnline A (2016/11/14)
9u-tkbJPCja-zh2017/07/26 12:33:411465-31.80--33.1932.83- 0.00 0.00 0.00NMTNoNMT with SMT phrase translation (phrase extraction with branching entropy; attention over bidirectional LSTMs; by Harvard NMT)
10bjtu_nlpJPCja-zh2016/08/16 12:45:011150-31.49--32.7932.51- 0.00 0.00 0.00NMTNoRNN Encoder-Decoder with attention mechanism, single model
11ORGANIZERJPCja-zh2016/07/13 15:31:58968-31.05--32.3531.70- 0.00 0.00 0.00SMTNoString-to-Tree SMT
12ORGANIZERJPCja-zh2016/07/13 15:11:50966-30.60--32.0331.25- 0.00 0.00 0.00SMTNoPhrase-based SMT
13ORGANIZERJPCja-zh2016/07/13 15:16:28967-30.26--31.5730.91- 0.00 0.00 0.00SMTNoHierarchical Phrase-based SMT
14SenseJPCja-zh2016/08/29 01:08:541282-29.87--32.1130.75- 0.00 0.00 0.00SMTNoClustercat-C10-PBMT
15SenseJPCja-zh2016/08/29 09:52:571283-29.59--31.8430.46- 0.00 0.00 0.00SMTNoBaseline-C10-PBMT
16SenseJPCja-zh2016/08/29 23:10:281293-29.59--31.8830.44- 0.00 0.00 0.00SMTNoBaseline-C50-PBMT
17SenseJPCja-zh2016/08/30 08:15:191295-29.44--31.7130.36- 0.00 0.00 0.00SMTNoClustercat-C50-PBMT
18ORGANIZERJPCja-zh2016/07/26 10:35:571038-23.02--23.5723.29- 0.00 0.00 0.00OtherYesOnline A (2016)
19ORGANIZERJPCja-zh2016/08/08 17:57:511118-12.35--13.7213.17- 0.00 0.00 0.00OtherYesRBMT C (2016)
20ORGANIZERJPCja-zh2016/08/01 18:33:201069- 9.42-- 9.59 8.79- 0.00 0.00 0.00OtherYesOnline B (2016)

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RIBES


# Team Task Date/Time DataID RIBES
Method
Other
Resources
System
Description
juman kytea mecab moses-
tokenizer
stanford-
segmenter-
ctb
stanford-
segmenter-
pku
indic-
tokenizer
unuse myseg kmseg
1KNU_HyundaiJPCja-zh2019/07/27 08:37:113162-0.860783--0.8658520.864763----NMTYesTransformer(base) + *Used ASPEC corpus* with relative position, bt, r2l rerank, 4-model ensemble(9-check point)
2sarahJPCja-zh2019/07/26 11:39:522982-0.853579--0.8578050.857191----NMTNoTransformer, ensemble of 4 models
3ryanJPCja-zh2019/07/25 22:04:252950-0.852969--0.8582040.857643----NMTNoBase Transformer
4ORGANIZERJPCja-zh2018/08/15 18:14:011960-0.847112--0.8508510.850913--0.0000000.000000NMTNoNMT with Attention
5USTCJPCja-zh2018/08/31 16:54:462202-0.846472--0.8507500.849818--0.0000000.000000NMTNotensor2tensor, 4 model average, r2l rerank
6ORGANIZERJPCja-zh2016/11/16 11:16:251340-0.824829--0.8291220.829067-0.0000000.0000000.000000NMTYesOnline A (2016/11/14)
7u-tkbJPCja-zh2017/07/26 12:33:411465-0.819791--0.8260550.825025-0.0000000.0000000.000000NMTNoNMT with SMT phrase translation (phrase extraction with branching entropy; attention over bidirectional LSTMs; by Harvard NMT)
8bjtu_nlpJPCja-zh2016/08/16 12:45:011150-0.816577--0.8229780.820820-0.0000000.0000000.000000NMTNoRNN Encoder-Decoder with attention mechanism, single model
9NICT-2JPCja-zh2016/08/05 18:13:161106-0.811788--0.8203200.818701-0.0000000.0000000.000000SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC)
10NICT-2JPCja-zh2016/08/04 17:37:251081-0.808513--0.8179960.815322-0.0000000.0000000.000000SMTNoPhrase-based SMT with Preordering + Domain Adaptation
11ORGANIZERJPCja-zh2016/07/13 15:31:58968-0.793846--0.8028050.800848-0.0000000.0000000.000000SMTNoString-to-Tree SMT
12SenseJPCja-zh2016/08/29 01:08:541282-0.791760--0.8032880.798268-0.0000000.0000000.000000SMTNoClustercat-C10-PBMT
13SenseJPCja-zh2016/08/29 09:52:571283-0.790983--0.8034000.797822-0.0000000.0000000.000000SMTNoBaseline-C10-PBMT
14SenseJPCja-zh2016/08/30 08:15:191295-0.789417--0.8021230.796346-0.0000000.0000000.000000SMTNoClustercat-C50-PBMT
15SenseJPCja-zh2016/08/29 23:10:281293-0.788582--0.8013820.796637-0.0000000.0000000.000000SMTNoBaseline-C50-PBMT
16ORGANIZERJPCja-zh2016/07/13 15:16:28967-0.788415--0.7991180.796685-0.0000000.0000000.000000SMTNoHierarchical Phrase-based SMT
17ORGANIZERJPCja-zh2016/07/13 15:11:50966-0.787321--0.7978880.794388-0.0000000.0000000.000000SMTNoPhrase-based SMT
18ORGANIZERJPCja-zh2016/07/26 10:35:571038-0.754241--0.7606720.760148-0.0000000.0000000.000000OtherYesOnline A (2016)
19ORGANIZERJPCja-zh2016/08/08 17:57:511118-0.688240--0.7086810.700210-0.0000000.0000000.000000OtherYesRBMT C (2016)
20ORGANIZERJPCja-zh2016/08/01 18:33:201069-0.642026--0.6510700.643520-0.0000000.0000000.000000OtherYesOnline B (2016)

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AMFM


# Team Task Date/Time DataID AMFM
Method
Other
Resources
System
Description
juman kytea mecab moses-
tokenizer
stanford-
segmenter-
ctb
stanford-
segmenter-
pku
indic-
tokenizer
unuse myseg kmseg
1USTCJPCja-zh2018/08/31 16:54:462202-0.757690--0.7576900.757690--0.0000000.000000NMTNotensor2tensor, 4 model average, r2l rerank
2ORGANIZERJPCja-zh2018/08/15 18:14:011960-0.752360--0.7523600.752360--0.0000000.000000NMTNoNMT with Attention
3ORGANIZERJPCja-zh2016/11/16 11:16:251340-0.735470--0.7354700.735470-0.0000000.0000000.000000NMTYesOnline A (2016/11/14)
4NICT-2JPCja-zh2016/08/05 18:13:161106-0.731520--0.7315200.731520-0.0000000.0000000.000000SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC)
5NICT-2JPCja-zh2016/08/04 17:37:251081-0.723270--0.7232700.723270-0.0000000.0000000.000000SMTNoPhrase-based SMT with Preordering + Domain Adaptation
6ORGANIZERJPCja-zh2016/07/13 15:31:58968-0.720030--0.7200300.720030-0.0000000.0000000.000000SMTNoString-to-Tree SMT
7ORGANIZERJPCja-zh2016/07/13 15:16:28967-0.718360--0.7183600.718360-0.0000000.0000000.000000SMTNoHierarchical Phrase-based SMT
8ORGANIZERJPCja-zh2016/07/13 15:11:50966-0.710940--0.7109400.710940-0.0000000.0000000.000000SMTNoPhrase-based SMT
9SenseJPCja-zh2016/08/30 08:15:191295-0.710150--0.7101500.710150-0.0000000.0000000.000000SMTNoClustercat-C50-PBMT
10SenseJPCja-zh2016/08/29 23:10:281293-0.710030--0.7100300.710030-0.0000000.0000000.000000SMTNoBaseline-C50-PBMT
11SenseJPCja-zh2016/08/29 01:08:541282-0.709390--0.7093900.709390-0.0000000.0000000.000000SMTNoClustercat-C10-PBMT
12SenseJPCja-zh2016/08/29 09:52:571283-0.707590--0.7075900.707590-0.0000000.0000000.000000SMTNoBaseline-C10-PBMT
13u-tkbJPCja-zh2017/07/26 12:33:411465-0.706720--0.7067200.706720-0.0000000.0000000.000000NMTNoNMT with SMT phrase translation (phrase extraction with branching entropy; attention over bidirectional LSTMs; by Harvard NMT)
14ORGANIZERJPCja-zh2016/07/26 10:35:571038-0.702350--0.7023500.702350-0.0000000.0000000.000000OtherYesOnline A (2016)
15bjtu_nlpJPCja-zh2016/08/16 12:45:011150-0.701490--0.7014900.701490-0.0000000.0000000.000000NMTNoRNN Encoder-Decoder with attention mechanism, single model
16ORGANIZERJPCja-zh2016/08/01 18:33:201069-0.527180--0.5271800.527180-0.0000000.0000000.000000OtherYesOnline B (2016)
17ORGANIZERJPCja-zh2016/08/08 17:57:511118-0.475430--0.4754300.475430-0.0000000.0000000.000000OtherYesRBMT C (2016)
18ryanJPCja-zh2019/07/25 22:04:252950-0.000000--0.0000000.000000----NMTNoBase Transformer
19sarahJPCja-zh2019/07/26 11:39:522982-0.000000--0.0000000.000000----NMTNoTransformer, ensemble of 4 models
20KNU_HyundaiJPCja-zh2019/07/27 08:37:113162-0.000000--0.0000000.000000----NMTYesTransformer(base) + *Used ASPEC corpus* with relative position, bt, r2l rerank, 4-model ensemble(9-check point)

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HUMAN (WAT2019)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1sarahJPCja-zh2019/07/26 11:39:522982UnderwayNMTNoTransformer, ensemble of 4 models
2KNU_HyundaiJPCja-zh2019/07/27 08:37:113162UnderwayNMTYesTransformer(base) + *Used ASPEC corpus* with relative position, bt, r2l rerank, 4-model ensemble(9-check point)

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HUMAN (WAT2018)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description

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HUMAN (WAT2017)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1u-tkbJPCja-zh2017/07/26 12:33:41146521.750NMTNoNMT with SMT phrase translation (phrase extraction with branching entropy; attention over bidirectional LSTMs; by Harvard NMT)

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HUMAN (WAT2016)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1ORGANIZERJPCja-zh2016/11/16 11:16:25134032.500NMTYesOnline A (2016/11/14)
2NICT-2JPCja-zh2016/08/05 18:13:16110614.000SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC)
3ORGANIZERJPCja-zh2016/07/13 15:16:289674.750SMTNoHierarchical Phrase-based SMT
4ORGANIZERJPCja-zh2016/07/13 15:31:589684.250SMTNoString-to-Tree SMT
5bjtu_nlpJPCja-zh2016/08/16 12:45:011150-1.000NMTNoRNN Encoder-Decoder with attention mechanism, single model
6NICT-2JPCja-zh2016/08/04 17:37:251081-11.000SMTNoPhrase-based SMT with Preordering + Domain Adaptation
7ORGANIZERJPCja-zh2016/07/26 10:35:571038-23.000OtherYesOnline A (2016)
8ORGANIZERJPCja-zh2016/08/08 17:57:511118-41.250OtherYesRBMT C (2016)

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HUMAN (WAT2015)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description

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HUMAN (WAT2014)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description

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EVALUATION RESULTS USAGE POLICY

When you use the WAT evaluation results for any purpose such as:
- writing technical papers,
- making presentations about your system,
- advertising your MT system to the customers,
you can use the information about translation directions, scores (including both automatic and human evaluations) and ranks of your system among others. You can also use the scores of the other systems, but you MUST anonymize the other system's names. In addition, you can show the links (URLs) to the WAT evaluation result pages.

NICT (National Institute of Information and Communications Technology)
Kyoto University
Last Modified: 2018-08-02