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WAT

The Workshop on Asian Translation
Evaluation Results

[EVALUATION RESULTS TOP] | [BLEU] | [RIBES] | [AMFM] | [HUMAN (WAT2022)] | [HUMAN (WAT2021)] | [HUMAN (WAT2020)] | [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
1NITRINDIC22en-as2022/07/26 20:47:587016------10.20---NMTNoMultilingual One to Many (En-XX) model trained on WAT2022 corpus based on Transformer with shared encoder and decoder using ensemble techniques.
2CNLP-NITS-PPINDIC22en-as2022/07/18 18:14:456965------ 1.10---NMTYes Trained multilingual-NMT model for En-to-X with language tag

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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
1NITRINDIC22en-as2022/07/26 20:47:587016------0.634631---NMTNoMultilingual One to Many (En-XX) model trained on WAT2022 corpus based on Transformer with shared encoder and decoder using ensemble techniques.
2CNLP-NITS-PPINDIC22en-as2022/07/18 18:14:456965------0.359265---NMTYes Trained multilingual-NMT model for En-to-X with language tag

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AMFM


# Team Task Date/Time DataID AMFM
Method
Other
Resources
System
Description
unuse unuse unuse unuse unuse unuse unuse unuse unuse unuse
1CNLP-NITS-PPINDIC22en-as2022/07/18 18:14:456965------0.000000---NMTYes Trained multilingual-NMT model for En-to-X with language tag
2NITRINDIC22en-as2022/07/26 20:47:587016------0.000000---NMTNoMultilingual One to Many (En-XX) model trained on WAT2022 corpus based on Transformer with shared encoder and decoder using ensemble techniques.

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HUMAN (WAT2022)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1NITRINDIC22en-as2022/07/26 20:47:5870163.445NMTNoMultilingual One to Many (En-XX) model trained on WAT2022 corpus based on Transformer with shared encoder and decoder using ensemble techniques.
2CNLP-NITS-PPINDIC22en-as2022/07/18 18:14:4569651.048NMTYes Trained multilingual-NMT model for En-to-X with language tag

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HUMAN (WAT2021)


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

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HUMAN (WAT2020)


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

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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

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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
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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