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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
1cvitHINDENen-hi2018/09/18 22:37:372500------21.57- 0.00 0.00NMTYesAveraging Models from epochs 61-68. Base Transformer. Uses External Data.
2XMUNLPHINDENen-hi2017/07/28 23:38:291576------21.39 0.00 0.00 0.00NMTNoensemble of 4 nmt models + monolingual data
3cvitHINDENen-hi2018/09/18 21:58:212496------21.35- 0.00 0.00NMTYesTransformer Base. Uses External Data. Averaging of Checkpoints Enabled.
4cvitHINDENen-hi2018/09/18 15:21:132489------21.10- 0.00 0.00NMTYesTransformer Base. Uses External Data
5cvitHINDENen-hi2019/05/27 16:03:362680------20.46- 0.00 0.00NMTYesmassive-multi + bt
6CUNIHINDENen-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
7cvitHINDENen-hi2019/03/15 01:21:272642------20.17- 0.00 0.00NMTYesmassive-multi
8CUNIHINDENen-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
9XMUNLPHINDENen-hi2017/07/27 22:04:541508------19.79 0.00 0.00 0.00NMTNosingle nmt model + monolingual data
10CUNIHINDENen-hi2018/09/13 22:18:142320------19.78- 0.00 0.00NMTNoBig Transformer model with backtranslation, with transfer learning from English to Czech.
11cvitHINDENen-hi2018/09/09 21:12:292254------19.69- 0.00 0.00NMTYesConvS2S. Uses external data.
12cvitHINDENen-hi2018/09/07 12:29:042235------18.77- 0.00 0.00NMTYesConvS2S Model. External Data is used.
13ORGANIZERHINDENen-hi2016/07/26 10:07:481032------18.72 0.00 0.00 0.00OtherYesOnline A (2016)
14cvitHINDENen-hi2019/03/15 01:31:412644------18.31- 0.00 0.00NMTYesmassive-multi + ft
15CUNIHINDENen-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.
16ORGANIZERHINDENen-hi2016/07/26 13:24:221047------16.97 0.00 0.00 0.00OtherYesOnline B (2016)
17cvitHINDENen-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]
18CUNIHINDENen-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.
19CUNIHINDENen-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
20ORGANIZERHINDENen-hi2018/11/13 14:54:582566------13.76- 0.00 0.00NMTNoNMT with Attention
21IITP-MTHINDENen-hi2016/08/18 23:13:251185------13.71 0.00 0.00 0.00SMTYesIITP-MT System1
22XMUNLPHINDENen-hi2017/07/20 23:07:381422------13.69 0.00 0.00 0.00NMTNosingle nmt model
23IITP-MTHINDENen-hi2016/08/29 18:51:441290------13.57 0.00 0.00 0.00SMTNoIITP-MT System2
24IITB-MTGHINDENen-hi2017/08/01 15:09:011725------12.23 0.00 0.00 0.00NMTNoNMT with ensemble (last 3 + best validation)
25EHRHINDENen-hi2016/08/17 14:30:081166------11.75 0.00 0.00 0.00SMTNoPBSMT with preordering (DL=6)
26ORGANIZERHINDENen-hi2016/08/20 17:41:361252------10.79 0.00 0.00 0.00SMTNoPhrase-based SMT
27IITB-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
4cvitHINDENen-hi2019/05/27 16:03:362680------0.765422-0.0000000.000000NMTYesmassive-multi + bt
5CUNIHINDENen-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
6CUNIHINDENen-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
7cvitHINDENen-hi2019/03/15 01:21:272642------0.761061-0.0000000.000000NMTYesmassive-multi
8cvitHINDENen-hi2018/09/09 21:12:292254------0.758365-0.0000000.000000NMTYesConvS2S. Uses external data.
9CUNIHINDENen-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.
10CUNIHINDENen-hi2018/09/13 22:18:142320------0.754244-0.0000000.000000NMTNoBig Transformer model with backtranslation, with transfer learning from English to Czech.
11CUNIHINDENen-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.
12XMUNLPHINDENen-hi2017/07/28 23:38:291576------0.7496600.0000000.0000000.000000NMTNoensemble of 4 nmt models + monolingual data
13cvitHINDENen-hi2018/09/07 12:29:042235------0.748008-0.0000000.000000NMTYesConvS2S Model. External Data is used.
14XMUNLPHINDENen-hi2017/07/27 22:04:541508------0.7431290.0000000.0000000.000000NMTNosingle nmt model + monolingual data
15CUNIHINDENen-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
16cvitHINDENen-hi2019/03/15 01:31:412644------0.718374-0.0000000.000000NMTYesmassive-multi + ft
17ORGANIZERHINDENen-hi2016/07/26 10:07:481032------0.7167880.0000000.0000000.000000OtherYesOnline A (2016)
18cvitHINDENen-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]
19XMUNLPHINDENen-hi2017/07/20 23:07:381422------0.7128760.0000000.0000000.000000NMTNosingle nmt model
20ORGANIZERHINDENen-hi2018/11/13 14:54:582566------0.710210-0.0000000.000000NMTNoNMT with Attention
21ORGANIZERHINDENen-hi2016/07/26 13:24:221047------0.6912980.0000000.0000000.000000OtherYesOnline B (2016)
22IITP-MTHINDENen-hi2016/08/18 23:13:251185------0.6889130.0000000.0000000.000000SMTYesIITP-MT System1
23IITB-MTGHINDENen-hi2017/08/01 15:09:011725------0.6886060.0000000.0000000.000000NMTNoNMT with ensemble (last 3 + best validation)
24IITP-MTHINDENen-hi2016/08/29 18:51:441290------0.6830220.0000000.0000000.000000SMTNoIITP-MT System2
25EHRHINDENen-hi2016/08/17 14:30:081166------0.6718660.0000000.0000000.000000SMTNoPBSMT with preordering (DL=6)
26ORGANIZERHINDENen-hi2016/08/20 17:41:361252------0.6511660.0000000.0000000.000000SMTNoPhrase-based SMT
27IITB-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
1cvitHINDENen-hi2018/09/18 15:21:132489------0.712200-0.0000000.000000NMTYesTransformer Base. Uses External Data
2cvitHINDENen-hi2018/09/18 22:37:372500------0.712110-0.0000000.000000NMTYesAveraging Models from epochs 61-68. Base Transformer. Uses External Data.
3cvitHINDENen-hi2018/09/18 21:58:212496------0.712010-0.0000000.000000NMTYesTransformer Base. Uses External Data. Averaging of Checkpoints Enabled.
4CUNIHINDENen-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
5cvitHINDENen-hi2019/05/27 16:03:362680------0.702380-0.0000000.000000NMTYesmassive-multi + bt
6cvitHINDENen-hi2019/03/15 01:21:272642------0.701670-0.0000000.000000NMTYesmassive-multi
7CUNIHINDENen-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
8CUNIHINDENen-hi2018/09/13 22:18:142320------0.700240-0.0000000.000000NMTNoBig Transformer model with backtranslation, with transfer learning from English to Czech.
9cvitHINDENen-hi2018/09/09 21:12:292254------0.699810-0.0000000.000000NMTYesConvS2S. Uses external data.
10cvitHINDENen-hi2018/09/07 12:29:042235------0.697630-0.0000000.000000NMTYesConvS2S Model. External Data is used.
11CUNIHINDENen-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.
12CUNIHINDENen-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.
13XMUNLPHINDENen-hi2017/07/28 23:38:291576------0.6887700.0000000.0000000.000000NMTNoensemble of 4 nmt models + monolingual data
14XMUNLPHINDENen-hi2017/07/27 22:04:541508------0.6825000.0000000.0000000.000000NMTNosingle nmt model + monolingual data
15CUNIHINDENen-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
16cvitHINDENen-hi2019/03/15 01:31:412644------0.680620-0.0000000.000000NMTYesmassive-multi + ft
17ORGANIZERHINDENen-hi2016/07/26 10:07:481032------0.6706600.0000000.0000000.000000OtherYesOnline A (2016)
18ORGANIZERHINDENen-hi2016/07/26 13:24:221047------0.6684500.0000000.0000000.000000OtherYesOnline B (2016)
19cvitHINDENen-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]
20IITP-MTHINDENen-hi2016/08/29 18:51:441290------0.6632100.0000000.0000000.000000SMTNoIITP-MT System2
21ORGANIZERHINDENen-hi2016/08/20 17:41:361252------0.6608600.0000000.0000000.000000SMTNoPhrase-based SMT
22IITP-MTHINDENen-hi2016/08/18 23:13:251185------0.6573300.0000000.0000000.000000SMTYesIITP-MT System1
23EHRHINDENen-hi2016/08/17 14:30:081166------0.6507500.0000000.0000000.000000SMTNoPBSMT with preordering (DL=6)
24XMUNLPHINDENen-hi2017/07/20 23:07:381422------0.6477400.0000000.0000000.000000NMTNosingle nmt model
25ORGANIZERHINDENen-hi2018/11/13 14:54:582566------0.644860-0.0000000.000000NMTNoNMT with Attention
26IITB-MTGHINDENen-hi2017/08/01 15:09:011725------0.6247800.0000000.0000000.000000NMTNoNMT with ensemble (last 3 + best validation)
27IITB-MTGHINDENen-hi2017/09/05 23:04:581763------0.4633500.0000000.0000000.000000NMTNo

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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