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WAT

The Workshop on Asian Translation
Evaluation Results

[EVALUATION RESULTS TOP] | [BLEU] | [RIBES] | [AMFM] | [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
1WTHINDENen-hi2020/09/03 23:14:363640------22.80---NMTYesMultilingual-ensembleX3
2WTHINDENen-hi2020/09/03 18:20:143639------22.08---NMTNoUsed 5M Back translation news crawl data to train. Method: Transformer NMT; Preprocessing: 1. Removed mixed language sentences 2. moses tokeniser for English and for Hindi indicnlp normaliser and toke
3cvitHINDENen-hi2018/09/18 22:37:372500------21.57- 0.00 0.00NMTYesAveraging Models from epochs 61-68. Base Transformer. Uses External Data.
4XMUNLPHINDENen-hi2017/07/28 23:38:291576------21.39 0.00 0.00 0.00NMTNoensemble of 4 nmt models + monolingual data
5cvitHINDENen-hi2018/09/18 21:58:212496------21.35- 0.00 0.00NMTYesTransformer Base. Uses External Data. Averaging of Checkpoints Enabled.
6cvitHINDENen-hi2018/09/18 15:21:132489------21.10- 0.00 0.00NMTYesTransformer Base. Uses External Data
7cvitHINDENen-hi2020/07/10 04:40:193436------20.69---NMTYesMultilingual model, uses pib-v2 data
8cvitHINDENen-hi2020/07/06 19:22:293428------20.52---NMTYesMultilingual Transformer model. Uses pib-v0 data.
9NICT-5HINDENen-hi2020/09/18 17:47:183935------20.48---NMTNoMBART Fine Tune on approx. 900k sentence pairs from whole HindEn dataset.
10cvitHINDENen-hi2019/05/27 16:03:362680------20.46- 0.00 0.00NMTYesmassive-multi + bt
11CUNIHINDENen-hi2018/09/15 01:12:402361------20.28- 0.00 0.00NMTNoTransformer big, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 1300k steps
12cvitHINDENen-hi2019/03/15 01:21:272642------20.17- 0.00 0.00NMTYesmassive-multi
13CUNIHINDENen-hi2018/09/15 01:22:032365------20.07- 0.00 0.00NMTNoTransformer big, transfer learning from EN-CS 1M steps, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 700k steps
14cvitHINDENen-hi2020/07/06 19:08:493427------19.83---NMTYesMultilingual Transformer model.
15XMUNLPHINDENen-hi2017/07/27 22:04:541508------19.79 0.00 0.00 0.00NMTNosingle nmt model + monolingual data
16CUNIHINDENen-hi2018/09/13 22:18:142320------19.78- 0.00 0.00NMTNoBig Transformer model with backtranslation, with transfer learning from English to Czech.
17cvitHINDENen-hi2018/09/09 21:12:292254------19.69- 0.00 0.00NMTYesConvS2S. Uses external data.
18cvitHINDENen-hi2018/09/07 12:29:042235------18.77- 0.00 0.00NMTYesConvS2S Model. External Data is used.
19ORGANIZERHINDENen-hi2016/07/26 10:07:481032------18.72 0.00 0.00 0.00OtherYesOnline A (2016)
20cvitHINDENen-hi2019/03/15 01:31:412644------18.31- 0.00 0.00NMTYesmassive-multi + ft
21CUNIHINDENen-hi2018/09/15 01:14:342362------17.63- 0.00 0.00NMTNoTransformer big, transfer learning from EN-CS 1M steps, followed by only backtranslation EN-HI for 300k steps, followed by original EN-HI for 500k steps, beam=8; alpha=0.8; averaging of last 8 models.
22ORGANIZERHINDENen-hi2016/07/26 13:24:221047------16.97 0.00 0.00 0.00OtherYesOnline B (2016)
23cvitHINDENen-hi2018/09/09 01:20:092251------16.77- 0.00 0.00NMTNoConvS2S Model. IIT-Bombay data filtered with langdetect. + Backtranslated Monolingual Data ppl in [0.05, 0.14]
24CUNIHINDENen-hi2018/09/15 01:19:042363------16.49- 0.00 0.00NMTNoTransformer big, transfer learning from EN-CS 1M steps, only original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 230k steps.
25CUNIHINDENen-hi2018/09/15 01:20:332364------14.20- 0.00 0.00NMTNoBaseline, transformer big only EN-HI, beam=8, alpha=0.8, averaging 8 steps after 330k steps
26ORGANIZERHINDENen-hi2018/11/13 14:54:582566------13.76- 0.00 0.00NMTNoNMT with Attention
27IITP-MTHINDENen-hi2016/08/18 23:13:251185------13.71 0.00 0.00 0.00SMTYesIITP-MT System1
28XMUNLPHINDENen-hi2017/07/20 23:07:381422------13.69 0.00 0.00 0.00NMTNosingle nmt model
29IITP-MTHINDENen-hi2016/08/29 18:51:441290------13.57 0.00 0.00 0.00SMTNoIITP-MT System2
30IITB-MTGHINDENen-hi2017/08/01 15:09:011725------12.23 0.00 0.00 0.00NMTNoNMT with ensemble (last 3 + best validation)
31EHRHINDENen-hi2016/08/17 14:30:081166------11.75 0.00 0.00 0.00SMTNoPBSMT with preordering (DL=6)
32ORGANIZERHINDENen-hi2016/08/20 17:41:361252------10.79 0.00 0.00 0.00SMTNoPhrase-based SMT
33IITB-MTGHINDENen-hi2017/09/05 23:04:581763------ 0.34 0.00 0.00 0.00NMTNo

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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
1cvitHINDENen-hi2018/09/18 22:37:372500------0.773923-0.0000000.000000NMTYesAveraging Models from epochs 61-68. Base Transformer. Uses External Data.
2cvitHINDENen-hi2018/09/18 21:58:212496------0.773078-0.0000000.000000NMTYesTransformer Base. Uses External Data. Averaging of Checkpoints Enabled.
3cvitHINDENen-hi2018/09/18 15:21:132489------0.771549-0.0000000.000000NMTYesTransformer Base. Uses External Data
4WTHINDENen-hi2020/09/03 23:14:363640------0.769138---NMTYesMultilingual-ensembleX3
5cvitHINDENen-hi2020/07/06 19:22:293428------0.766753---NMTYesMultilingual Transformer model. Uses pib-v0 data.
6cvitHINDENen-hi2019/05/27 16:03:362680------0.765422-0.0000000.000000NMTYesmassive-multi + bt
7WTHINDENen-hi2020/09/03 18:20:143639------0.765340---NMTNoUsed 5M Back translation news crawl data to train. Method: Transformer NMT; Preprocessing: 1. Removed mixed language sentences 2. moses tokeniser for English and for Hindi indicnlp normaliser and toke
8cvitHINDENen-hi2020/07/10 04:40:193436------0.764496---NMTYesMultilingual model, uses pib-v2 data
9NICT-5HINDENen-hi2020/09/18 17:47:183935------0.763000---NMTNoMBART Fine Tune on approx. 900k sentence pairs from whole HindEn dataset.
10CUNIHINDENen-hi2018/09/15 01:22:032365------0.761582-0.0000000.000000NMTNoTransformer big, transfer learning from EN-CS 1M steps, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 700k steps
11CUNIHINDENen-hi2018/09/15 01:12:402361------0.761292-0.0000000.000000NMTNoTransformer big, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 1300k steps
12cvitHINDENen-hi2019/03/15 01:21:272642------0.761061-0.0000000.000000NMTYesmassive-multi
13cvitHINDENen-hi2020/07/06 19:08:493427------0.758405---NMTYesMultilingual Transformer model.
14cvitHINDENen-hi2018/09/09 21:12:292254------0.758365-0.0000000.000000NMTYesConvS2S. Uses external data.
15CUNIHINDENen-hi2018/09/15 01:19:042363------0.754966-0.0000000.000000NMTNoTransformer big, transfer learning from EN-CS 1M steps, only original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 230k steps.
16CUNIHINDENen-hi2018/09/13 22:18:142320------0.754244-0.0000000.000000NMTNoBig Transformer model with backtranslation, with transfer learning from English to Czech.
17CUNIHINDENen-hi2018/09/15 01:14:342362------0.753895-0.0000000.000000NMTNoTransformer big, transfer learning from EN-CS 1M steps, followed by only backtranslation EN-HI for 300k steps, followed by original EN-HI for 500k steps, beam=8; alpha=0.8; averaging of last 8 models.
18XMUNLPHINDENen-hi2017/07/28 23:38:291576------0.7496600.0000000.0000000.000000NMTNoensemble of 4 nmt models + monolingual data
19cvitHINDENen-hi2018/09/07 12:29:042235------0.748008-0.0000000.000000NMTYesConvS2S Model. External Data is used.
20XMUNLPHINDENen-hi2017/07/27 22:04:541508------0.7431290.0000000.0000000.000000NMTNosingle nmt model + monolingual data
21CUNIHINDENen-hi2018/09/15 01:20:332364------0.733738-0.0000000.000000NMTNoBaseline, transformer big only EN-HI, beam=8, alpha=0.8, averaging 8 steps after 330k steps
22cvitHINDENen-hi2019/03/15 01:31:412644------0.718374-0.0000000.000000NMTYesmassive-multi + ft
23ORGANIZERHINDENen-hi2016/07/26 10:07:481032------0.7167880.0000000.0000000.000000OtherYesOnline A (2016)
24cvitHINDENen-hi2018/09/09 01:20:092251------0.714197-0.0000000.000000NMTNoConvS2S Model. IIT-Bombay data filtered with langdetect. + Backtranslated Monolingual Data ppl in [0.05, 0.14]
25XMUNLPHINDENen-hi2017/07/20 23:07:381422------0.7128760.0000000.0000000.000000NMTNosingle nmt model
26ORGANIZERHINDENen-hi2018/11/13 14:54:582566------0.710210-0.0000000.000000NMTNoNMT with Attention
27ORGANIZERHINDENen-hi2016/07/26 13:24:221047------0.6912980.0000000.0000000.000000OtherYesOnline B (2016)
28IITP-MTHINDENen-hi2016/08/18 23:13:251185------0.6889130.0000000.0000000.000000SMTYesIITP-MT System1
29IITB-MTGHINDENen-hi2017/08/01 15:09:011725------0.6886060.0000000.0000000.000000NMTNoNMT with ensemble (last 3 + best validation)
30IITP-MTHINDENen-hi2016/08/29 18:51:441290------0.6830220.0000000.0000000.000000SMTNoIITP-MT System2
31EHRHINDENen-hi2016/08/17 14:30:081166------0.6718660.0000000.0000000.000000SMTNoPBSMT with preordering (DL=6)
32ORGANIZERHINDENen-hi2016/08/20 17:41:361252------0.6511660.0000000.0000000.000000SMTNoPhrase-based SMT
33IITB-MTGHINDENen-hi2017/09/05 23:04:581763------0.3012410.0000000.0000000.000000NMTNo

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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
1WTHINDENen-hi2020/09/03 23:14:363640------0.873830---NMTYesMultilingual-ensembleX3
2WTHINDENen-hi2020/09/03 18:20:143639------0.869400---NMTNoUsed 5M Back translation news crawl data to train. Method: Transformer NMT; Preprocessing: 1. Removed mixed language sentences 2. moses tokeniser for English and for Hindi indicnlp normaliser and toke
3cvitHINDENen-hi2020/07/10 04:40:193436------0.868770---NMTYesMultilingual model, uses pib-v2 data
4cvitHINDENen-hi2020/07/06 19:08:493427------0.867360---NMTYesMultilingual Transformer model.
5cvitHINDENen-hi2020/07/06 19:22:293428------0.866410---NMTYesMultilingual Transformer model. Uses pib-v0 data.
6NICT-5HINDENen-hi2020/09/18 17:47:183935------0.864600---NMTNoMBART Fine Tune on approx. 900k sentence pairs from whole HindEn dataset.
7cvitHINDENen-hi2018/09/18 15:21:132489------0.712200-0.0000000.000000NMTYesTransformer Base. Uses External Data
8cvitHINDENen-hi2018/09/18 22:37:372500------0.712110-0.0000000.000000NMTYesAveraging Models from epochs 61-68. Base Transformer. Uses External Data.
9cvitHINDENen-hi2018/09/18 21:58:212496------0.712010-0.0000000.000000NMTYesTransformer Base. Uses External Data. Averaging of Checkpoints Enabled.
10CUNIHINDENen-hi2018/09/15 01:12:402361------0.704220-0.0000000.000000NMTNoTransformer big, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 1300k steps
11cvitHINDENen-hi2019/05/27 16:03:362680------0.702380-0.0000000.000000NMTYesmassive-multi + bt
12cvitHINDENen-hi2019/03/15 01:21:272642------0.701670-0.0000000.000000NMTYesmassive-multi
13CUNIHINDENen-hi2018/09/15 01:22:032365------0.701300-0.0000000.000000NMTNoTransformer big, transfer learning from EN-CS 1M steps, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 700k steps
14CUNIHINDENen-hi2018/09/13 22:18:142320------0.700240-0.0000000.000000NMTNoBig Transformer model with backtranslation, with transfer learning from English to Czech.
15cvitHINDENen-hi2018/09/09 21:12:292254------0.699810-0.0000000.000000NMTYesConvS2S. Uses external data.
16cvitHINDENen-hi2018/09/07 12:29:042235------0.697630-0.0000000.000000NMTYesConvS2S Model. External Data is used.
17CUNIHINDENen-hi2018/09/15 01:14:342362------0.693830-0.0000000.000000NMTNoTransformer big, transfer learning from EN-CS 1M steps, followed by only backtranslation EN-HI for 300k steps, followed by original EN-HI for 500k steps, beam=8; alpha=0.8; averaging of last 8 models.
18CUNIHINDENen-hi2018/09/15 01:19:042363------0.690150-0.0000000.000000NMTNoTransformer big, transfer learning from EN-CS 1M steps, only original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 230k steps.
19XMUNLPHINDENen-hi2017/07/28 23:38:291576------0.6887700.0000000.0000000.000000NMTNoensemble of 4 nmt models + monolingual data
20XMUNLPHINDENen-hi2017/07/27 22:04:541508------0.6825000.0000000.0000000.000000NMTNosingle nmt model + monolingual data
21CUNIHINDENen-hi2018/09/15 01:20:332364------0.681460-0.0000000.000000NMTNoBaseline, transformer big only EN-HI, beam=8, alpha=0.8, averaging 8 steps after 330k steps
22cvitHINDENen-hi2019/03/15 01:31:412644------0.680620-0.0000000.000000NMTYesmassive-multi + ft
23ORGANIZERHINDENen-hi2016/07/26 10:07:481032------0.6706600.0000000.0000000.000000OtherYesOnline A (2016)
24ORGANIZERHINDENen-hi2016/07/26 13:24:221047------0.6684500.0000000.0000000.000000OtherYesOnline B (2016)
25cvitHINDENen-hi2018/09/09 01:20:092251------0.664330-0.0000000.000000NMTNoConvS2S Model. IIT-Bombay data filtered with langdetect. + Backtranslated Monolingual Data ppl in [0.05, 0.14]
26IITP-MTHINDENen-hi2016/08/29 18:51:441290------0.6632100.0000000.0000000.000000SMTNoIITP-MT System2
27ORGANIZERHINDENen-hi2016/08/20 17:41:361252------0.6608600.0000000.0000000.000000SMTNoPhrase-based SMT
28IITP-MTHINDENen-hi2016/08/18 23:13:251185------0.6573300.0000000.0000000.000000SMTYesIITP-MT System1
29EHRHINDENen-hi2016/08/17 14:30:081166------0.6507500.0000000.0000000.000000SMTNoPBSMT with preordering (DL=6)
30XMUNLPHINDENen-hi2017/07/20 23:07:381422------0.6477400.0000000.0000000.000000NMTNosingle nmt model
31ORGANIZERHINDENen-hi2018/11/13 14:54:582566------0.644860-0.0000000.000000NMTNoNMT with Attention
32IITB-MTGHINDENen-hi2017/08/01 15:09:011725------0.6247800.0000000.0000000.000000NMTNoNMT with ensemble (last 3 + best validation)
33IITB-MTGHINDENen-hi2017/09/05 23:04:581763------0.4633500.0000000.0000000.000000NMTNo

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HUMAN (WAT2020)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1WTHINDENen-hi2020/09/03 18:20:143639UnderwayNMTNoUsed 5M Back translation news crawl data to train. Method: Transformer NMT; Preprocessing: 1. Removed mixed language sentences 2. moses tokeniser for English and for Hindi indicnlp normaliser and toke
2WTHINDENen-hi2020/09/03 23:14:363640UnderwayNMTYesMultilingual-ensembleX3

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HUMAN (WAT2019)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1cvitHINDENen-hi2019/05/27 16:03:362680UnderwayNMTYesmassive-multi + bt

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HUMAN (WAT2018)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1CUNIHINDENen-hi2018/09/15 01:14:34236277.000NMTNoTransformer big, transfer learning from EN-CS 1M steps, followed by only backtranslation EN-HI for 300k steps, followed by original EN-HI for 500k steps, beam=8; alpha=0.8; averaging of last 8 models.
2cvitHINDENen-hi2018/09/09 21:12:29225469.500NMTYesConvS2S. Uses external data.
3CUNIHINDENen-hi2018/09/15 01:22:03236560.000NMTNoTransformer big, transfer learning from EN-CS 1M steps, only backtranslation EN-HI, no original EN-HI, beam=8; alpha=0.8; averaging of last 8 models after 700k steps
4cvitHINDENen-hi2018/09/09 01:20:09225150.500NMTNoConvS2S Model. IIT-Bombay data filtered with langdetect. + Backtranslated Monolingual Data ppl in [0.05, 0.14]
5cvitHINDENen-hi2018/09/07 12:29:042235UnderwayNMTYesConvS2S Model. External Data is used.

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HUMAN (WAT2017)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1XMUNLPHINDENen-hi2017/07/28 23:38:29157664.500NMTNoensemble of 4 nmt models + monolingual data
2IITB-MTGHINDENen-hi2017/08/01 15:09:01172528.750NMTNoNMT with ensemble (last 3 + best validation)

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HUMAN (WAT2016)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1ORGANIZERHINDENen-hi2016/07/26 10:07:48103257.250OtherYesOnline A (2016)
2ORGANIZERHINDENen-hi2016/07/26 13:24:22104742.500OtherYesOnline B (2016)
3IITP-MTHINDENen-hi2016/08/18 23:13:2511854.750SMTYesIITP-MT System1
4EHRHINDENen-hi2016/08/17 14:30:0811660.000SMTNoPBSMT with preordering (DL=6)

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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