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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
1RGNLPINDICen-hi2018/09/15 19:02:502417------44.08- 0.00 0.00SMTNoSMT system with KENLM Language model
2NICT-5INDICen-hi2018/08/22 19:09:342067------29.65- 0.00 0.00NMTNoEn-XX transformer model
3cvitINDICen-hi2019/03/23 17:12:472667------27.09- 0.00 0.00NMTYesmassive-multi e270
4IITP-MTINDICen-hi2018/09/14 20:18:112354------26.60- 0.00 0.00NMTNoTransformer multilingual En-XX
5NICT-5INDICen-hi2018/08/24 14:59:002128------26.59- 0.00 0.00NMTNoXX-XX transformer model
6AnuvaadINDICen-hi2018/09/16 02:47:442445------26.49- 0.00 0.00SMTNoSMT with KenLM
7NICT-5INDICen-hi2018/08/22 19:05:042062------26.35- 0.00 0.00SMTNoBilingual transformer model
8cvitINDICen-hi2019/03/14 22:53:452630------26.25- 0.00 0.00NMTYesmassive-multi + ft
9ORGANIZERINDICen-hi2018/08/24 18:20:352146------23.27- 0.00 0.00NMTNoone2multi multilingual NMT with Attention
10RGNLPINDICen-hi2018/09/15 20:30:182422------22.50- 0.00 0.00NMTNoNMT system with a 2-layer LSTM method
11ORGANIZERINDICen-hi2018/08/29 14:12:512188------22.24- 0.00 0.00NMTNomulti2multi multilingual NMT with Attention
12cvitINDICen-hi2018/10/18 14:05:552537------21.32- 0.00 0.00NMTYes
13ORGANIZERINDICen-hi2018/08/20 11:10:372003------20.78- 0.00 0.00NMTNoNMT with Attention
14cvitINDICen-hi2019/03/14 22:49:252629------20.72- 0.00 0.00NMTYesmassive-multi

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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
1RGNLPINDICen-hi2018/09/15 19:02:502417------0.751187-0.0000000.000000SMTNoSMT system with KENLM Language model
2cvitINDICen-hi2019/03/23 17:12:472667------0.745230-0.0000000.000000NMTYesmassive-multi e270
3cvitINDICen-hi2019/03/14 22:53:452630------0.740006-0.0000000.000000NMTYesmassive-multi + ft
4NICT-5INDICen-hi2018/08/24 14:59:002128------0.734027-0.0000000.000000NMTNoXX-XX transformer model
5IITP-MTINDICen-hi2018/09/14 20:18:112354------0.722756-0.0000000.000000NMTNoTransformer multilingual En-XX
6NICT-5INDICen-hi2018/08/22 19:09:342067------0.721379-0.0000000.000000NMTNoEn-XX transformer model
7cvitINDICen-hi2018/10/18 14:05:552537------0.714474-0.0000000.000000NMTYes
8cvitINDICen-hi2019/03/14 22:49:252629------0.712607-0.0000000.000000NMTYesmassive-multi
9ORGANIZERINDICen-hi2018/08/24 18:20:352146------0.709586-0.0000000.000000NMTNoone2multi multilingual NMT with Attention
10ORGANIZERINDICen-hi2018/08/29 14:12:512188------0.705747-0.0000000.000000NMTNomulti2multi multilingual NMT with Attention
11NICT-5INDICen-hi2018/08/22 19:05:042062------0.693875-0.0000000.000000SMTNoBilingual transformer model
12AnuvaadINDICen-hi2018/09/16 02:47:442445------0.692385-0.0000000.000000SMTNoSMT with KenLM
13ORGANIZERINDICen-hi2018/08/20 11:10:372003------0.682944-0.0000000.000000NMTNoNMT with Attention
14RGNLPINDICen-hi2018/09/15 20:30:182422------0.678207-0.0000000.000000NMTNoNMT system with a 2-layer LSTM method

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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
1RGNLPINDICen-hi2018/09/15 19:02:502417------0.698550-0.0000000.000000SMTNoSMT system with KENLM Language model
2AnuvaadINDICen-hi2018/09/16 02:47:442445------0.657180-0.0000000.000000SMTNoSMT with KenLM
3cvitINDICen-hi2019/03/14 22:53:452630------0.652810-0.0000000.000000NMTYesmassive-multi + ft
4cvitINDICen-hi2019/03/23 17:12:472667------0.652180-0.0000000.000000NMTYesmassive-multi e270
5NICT-5INDICen-hi2018/08/24 14:59:002128------0.636900-0.0000000.000000NMTNoXX-XX transformer model
6NICT-5INDICen-hi2018/08/22 19:09:342067------0.636500-0.0000000.000000NMTNoEn-XX transformer model
7cvitINDICen-hi2019/03/14 22:49:252629------0.632280-0.0000000.000000NMTYesmassive-multi
8cvitINDICen-hi2018/10/18 14:05:552537------0.629380-0.0000000.000000NMTYes
9IITP-MTINDICen-hi2018/09/14 20:18:112354------0.615620-0.0000000.000000NMTNoTransformer multilingual En-XX
10ORGANIZERINDICen-hi2018/08/29 14:12:512188------0.604040-0.0000000.000000NMTNomulti2multi multilingual NMT with Attention
11ORGANIZERINDICen-hi2018/08/24 18:20:352146------0.601800-0.0000000.000000NMTNoone2multi multilingual NMT with Attention
12NICT-5INDICen-hi2018/08/22 19:05:042062------0.589830-0.0000000.000000SMTNoBilingual transformer model
13RGNLPINDICen-hi2018/09/15 20:30:182422------0.584720-0.0000000.000000NMTNoNMT system with a 2-layer LSTM method
14ORGANIZERINDICen-hi2018/08/20 11:10:372003------0.574960-0.0000000.000000NMTNoNMT with Attention

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HUMAN (WAT2019)


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

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HUMAN (WAT2018)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1NICT-5INDICen-hi2018/08/24 14:59:00212830.750NMTNoXX-XX transformer model
2IITP-MTINDICen-hi2018/09/14 20:18:11235420.500NMTNoTransformer multilingual En-XX
3RGNLPINDICen-hi2018/09/15 19:02:50241715.500SMTNoSMT system with KENLM Language model
4NICT-5INDICen-hi2018/08/22 19:09:34206713.750NMTNoEn-XX transformer model
5AnuvaadINDICen-hi2018/09/16 02:47:44244511.000SMTNoSMT with KenLM
6RGNLPINDICen-hi2018/09/15 20:30:182422-0.250NMTNoNMT system with a 2-layer LSTM method

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HUMAN (WAT2017)


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

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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