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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
1BITS-PMMCHMM23en-bn2023/07/08 13:38:187122------48.70---NMTYesNLLB model finetuned on captions + object tags of original & synthetic images using DETR model
2ODIAGENMMCHMM23en-bn2023/07/06 04:05:407108------30.50---NMTNoImage features extracted as Object tags appended with text and MBART fine-tuning
3CNLP-NITS-PPMMCHMM23en-bn2022/07/11 12:54:096744------28.70---NMTNoTransliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
4SILO_NLPMMCHMM23en-bn2022/07/14 21:41:046940------28.70---NMTNoObject Tags (Image) + Finetune mBART

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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
1BITS-PMMCHMM23en-bn2023/07/08 13:38:187122------0.831946---NMTYesNLLB model finetuned on captions + object tags of original & synthetic images using DETR model
2ODIAGENMMCHMM23en-bn2023/07/06 04:05:407108------0.690706---NMTNoImage features extracted as Object tags appended with text and MBART fine-tuning
3CNLP-NITS-PPMMCHMM23en-bn2022/07/11 12:54:096744------0.688931---NMTNoTransliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
4SILO_NLPMMCHMM23en-bn2022/07/14 21:41:046940------0.666817---NMTNoObject Tags (Image) + Finetune mBART

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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-PPMMCHMM23en-bn2022/07/11 12:54:096744------0.000000---NMTNoTransliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
2SILO_NLPMMCHMM23en-bn2022/07/14 21:41:046940------0.000000---NMTNoObject Tags (Image) + Finetune mBART
3ODIAGENMMCHMM23en-bn2023/07/06 04:05:407108------0.000000---NMTNoImage features extracted as Object tags appended with text and MBART fine-tuning
4BITS-PMMCHMM23en-bn2023/07/08 13:38:187122------0.000000---NMTYesNLLB model finetuned on captions + object tags of original & synthetic images using DETR model

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HUMAN (WAT2022)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1CNLP-NITS-PPMMCHMM23en-bn2022/07/11 12:54:096744UnderwayNMTNoTransliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
2SILO_NLPMMCHMM23en-bn2022/07/14 21:41:046940UnderwayNMTNoObject Tags (Image) + Finetune mBART

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HUMAN (WAT2021)


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

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HUMAN (WAT2020)


# Team Task Date/Time DataID HUMAN
Method
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HUMAN (WAT2019)


# Team Task Date/Time DataID HUMAN
Method
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Description

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HUMAN (WAT2018)


# Team Task Date/Time DataID HUMAN
Method
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Description

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HUMAN (WAT2017)


# Team Task Date/Time DataID HUMAN
Method
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HUMAN (WAT2016)


# Team Task Date/Time DataID HUMAN
Method
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System
Description

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HUMAN (WAT2015)


# Team Task Date/Time DataID HUMAN
Method
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Description

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HUMAN (WAT2014)


# Team Task Date/Time DataID HUMAN
Method
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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