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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
1ORGANIZERJIJIja-en2017/07/19 12:51:031394---15.11--- 0.00 0.00 0.00SMTNoPhrase-based SMT
2ORGANIZERJIJIja-en2017/07/19 13:12:431396---15.67--- 0.00 0.00 0.00SMTNoHierarchical Phrase-based SMT
3ORGANIZERJIJIja-en2017/07/19 13:39:221398---14.54--- 0.00 0.00 0.00SMTNoString-to-Tree SMT
4XMUNLPJIJIja-en2017/07/24 09:02:371428---15.77--- 0.00 0.00 0.00NMTNosingle nmt model
5XMUNLPJIJIja-en2017/07/24 20:42:391442---17.95--- 0.00 0.00 0.00NMTNoensemble of 4 nmt models
6NICT-2JIJIja-en2017/07/26 13:46:111473---16.52--- 0.00 0.00 0.00NMTNoNMT Single Model: BPE35k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
7NICT-2JIJIja-en2017/07/26 13:49:391474---18.19--- 0.00 0.00 0.00NMTNoNMT 16 Ensembles * Bi-directional Reranking
8ORGANIZERJIJIja-en2017/07/28 15:24:581523--- 8.19--- 0.00 0.00 0.00NMTNoONLINE-A
9ORGANIZERJIJIja-en2017/07/28 15:25:471524--- 5.17--- 0.00 0.00 0.00NMTNoONLINE-B
10ORGANIZERJIJIja-en2017/07/28 15:26:561525--- 4.36--- 0.00 0.00 0.00RBMTNoRBMT-A
11ORGANIZERJIJIja-en2017/07/28 15:27:391526--- 4.67--- 0.00 0.00 0.00RBMTNoRBMT-B
12NTTJIJIja-en2017/07/28 19:24:021564---15.77--- 0.00 0.00 0.00NMTYesSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 5 w/ length-based reranking / initialized model = ASPEC + JPO
13NTTJIJIja-en2017/07/30 10:16:391599---19.44--- 0.00 0.00 0.00NMTNoSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
14CUNIJIJIja-en2017/07/31 22:38:531668---10.67--- 0.00 0.00 0.00SMTNoBahdanau (2014) seq2seq with conditional GRU on byte-pair encoding
15NTTJIJIja-en2017/08/01 02:55:311677---20.90--- 0.00 0.00 0.00NMTNoEnsemble 8 Models: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
16ORGANIZERJIJIja-en2018/08/14 11:49:181905---16.48---- 0.00 0.00NMTNoNMT with Attention
17sarahJIJIja-en2019/07/22 11:59:112793---21.84------NMTNoTransformer, ensemble of 4 models
18sarahJIJIja-en2019/07/22 16:41:432813---21.34------NMTNoTransformer, single model
19NHK-NESJIJIja-en2019/07/24 14:03:102883---26.38------NMTYesensemble-10, beamwidth-30 with other resources (including BT)
20NHK-NESJIJIja-en2019/07/24 14:07:562884---14.23------NMTYesensemble-10, beamwidth-30 using another domain-tag (including BT)

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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
1ORGANIZERJIJIja-en2017/07/19 12:51:031394---0.554550---0.0000000.0000000.000000SMTNoPhrase-based SMT
2ORGANIZERJIJIja-en2017/07/19 13:12:431396---0.558225---0.0000000.0000000.000000SMTNoHierarchical Phrase-based SMT
3ORGANIZERJIJIja-en2017/07/19 13:39:221398---0.556728---0.0000000.0000000.000000SMTNoString-to-Tree SMT
4XMUNLPJIJIja-en2017/07/24 09:02:371428---0.617123---0.0000000.0000000.000000NMTNosingle nmt model
5XMUNLPJIJIja-en2017/07/24 20:42:391442---0.637059---0.0000000.0000000.000000NMTNoensemble of 4 nmt models
6NICT-2JIJIja-en2017/07/26 13:46:111473---0.642379---0.0000000.0000000.000000NMTNoNMT Single Model: BPE35k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
7NICT-2JIJIja-en2017/07/26 13:49:391474---0.632638---0.0000000.0000000.000000NMTNoNMT 16 Ensembles * Bi-directional Reranking
8ORGANIZERJIJIja-en2017/07/28 15:24:581523---0.529844---0.0000000.0000000.000000NMTNoONLINE-A
9ORGANIZERJIJIja-en2017/07/28 15:25:471524---0.461632---0.0000000.0000000.000000NMTNoONLINE-B
10ORGANIZERJIJIja-en2017/07/28 15:26:561525---0.472312---0.0000000.0000000.000000RBMTNoRBMT-A
11ORGANIZERJIJIja-en2017/07/28 15:27:391526---0.475760---0.0000000.0000000.000000RBMTNoRBMT-B
12NTTJIJIja-en2017/07/28 19:24:021564---0.627182---0.0000000.0000000.000000NMTYesSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 5 w/ length-based reranking / initialized model = ASPEC + JPO
13NTTJIJIja-en2017/07/30 10:16:391599---0.638841---0.0000000.0000000.000000NMTNoSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
14CUNIJIJIja-en2017/07/31 22:38:531668---0.564797---0.0000000.0000000.000000SMTNoBahdanau (2014) seq2seq with conditional GRU on byte-pair encoding
15NTTJIJIja-en2017/08/01 02:55:311677---0.648931---0.0000000.0000000.000000NMTNoEnsemble 8 Models: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
16ORGANIZERJIJIja-en2018/08/14 11:49:181905---0.640558----0.0000000.000000NMTNoNMT with Attention
17sarahJIJIja-en2019/07/22 11:59:112793---0.675386------NMTNoTransformer, ensemble of 4 models
18sarahJIJIja-en2019/07/22 16:41:432813---0.676723------NMTNoTransformer, single model
19NHK-NESJIJIja-en2019/07/24 14:03:102883---0.703808------NMTYesensemble-10, beamwidth-30 with other resources (including BT)
20NHK-NESJIJIja-en2019/07/24 14:07:562884---0.612351------NMTYesensemble-10, beamwidth-30 using another domain-tag (including BT)

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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
1ORGANIZERJIJIja-en2017/07/19 12:51:031394---0.475740---0.0000000.0000000.000000SMTNoPhrase-based SMT
2ORGANIZERJIJIja-en2017/07/19 13:12:431396---0.470610---0.0000000.0000000.000000SMTNoHierarchical Phrase-based SMT
3ORGANIZERJIJIja-en2017/07/19 13:39:221398---0.477170---0.0000000.0000000.000000SMTNoString-to-Tree SMT
4XMUNLPJIJIja-en2017/07/24 09:02:371428---0.463550---0.0000000.0000000.000000NMTNosingle nmt model
5XMUNLPJIJIja-en2017/07/24 20:42:391442---0.465710---0.0000000.0000000.000000NMTNoensemble of 4 nmt models
6NICT-2JIJIja-en2017/07/26 13:46:111473---0.459000---0.0000000.0000000.000000NMTNoNMT Single Model: BPE35k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
7NICT-2JIJIja-en2017/07/26 13:49:391474---0.453420---0.0000000.0000000.000000NMTNoNMT 16 Ensembles * Bi-directional Reranking
8ORGANIZERJIJIja-en2017/07/28 15:24:581523---0.450850---0.0000000.0000000.000000NMTNoONLINE-A
9ORGANIZERJIJIja-en2017/07/28 15:25:471524---0.367760---0.0000000.0000000.000000NMTNoONLINE-B
10ORGANIZERJIJIja-en2017/07/28 15:26:561525---0.391050---0.0000000.0000000.000000RBMTNoRBMT-A
11ORGANIZERJIJIja-en2017/07/28 15:27:391526---0.385600---0.0000000.0000000.000000RBMTNoRBMT-B
12NTTJIJIja-en2017/07/28 19:24:021564---0.473970---0.0000000.0000000.000000NMTYesSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 5 w/ length-based reranking / initialized model = ASPEC + JPO
13NTTJIJIja-en2017/07/30 10:16:391599---0.476200---0.0000000.0000000.000000NMTNoSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
14CUNIJIJIja-en2017/07/31 22:38:531668---0.432700---0.0000000.0000000.000000SMTNoBahdanau (2014) seq2seq with conditional GRU on byte-pair encoding
15NTTJIJIja-en2017/08/01 02:55:311677---0.474360---0.0000000.0000000.000000NMTNoEnsemble 8 Models: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
16ORGANIZERJIJIja-en2018/08/14 11:49:181905---0.459080----0.0000000.000000NMTNoNMT with Attention
17sarahJIJIja-en2019/07/22 11:59:112793---0.526530------NMTNoTransformer, ensemble of 4 models
18sarahJIJIja-en2019/07/22 16:41:432813---0.524530------NMTNoTransformer, single model
19NHK-NESJIJIja-en2019/07/24 14:03:102883---0.554110------NMTYesensemble-10, beamwidth-30 with other resources (including BT)
20NHK-NESJIJIja-en2019/07/24 14:07:562884---0.526010------NMTYesensemble-10, beamwidth-30 using another domain-tag (including BT)

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HUMAN (WAT2019)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1NHK-NESJIJIja-en2019/07/24 14:07:56288489.000NMTYesensemble-10, beamwidth-30 using another domain-tag (including BT)
2NHK-NESJIJIja-en2019/07/24 14:03:10288372.000NMTYesensemble-10, beamwidth-30 with other resources (including BT)
3sarahJIJIja-en2019/07/22 11:59:11279350.750NMTNoTransformer, ensemble of 4 models
4sarahJIJIja-en2019/07/22 16:41:43281344.750NMTNoTransformer, single model

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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
1ORGANIZERJIJIja-en2017/07/28 15:24:58152370.000NMTNoONLINE-A
2ORGANIZERJIJIja-en2017/07/28 15:27:39152651.750RBMTNoRBMT-B
3NTTJIJIja-en2017/07/30 10:16:39159932.000NMTNoSingle Model: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
4NTTJIJIja-en2017/08/01 02:55:31167726.750NMTNoEnsemble 8 Models: joint BPE 16k, BiLSTM Encoder 512*2*2, LtoR LSTM Decoder 512*2, Beam Search 20 w/ length-based reranking
5XMUNLPJIJIja-en2017/07/24 20:42:39144220.750NMTNoensemble of 4 nmt models
6ORGANIZERJIJIja-en2017/07/19 13:12:43139610.250SMTNoHierarchical Phrase-based SMT
7NICT-2JIJIja-en2017/07/26 13:49:3914747.250NMTNoNMT 16 Ensembles * Bi-directional Reranking
8NICT-2JIJIja-en2017/07/26 13:46:1114730.250NMTNoNMT Single Model: BPE35k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
9CUNIJIJIja-en2017/07/31 22:38:531668-24.000SMTNoBahdanau (2014) seq2seq with conditional GRU on byte-pair encoding

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HUMAN (WAT2016)


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

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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