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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
1srcbzh-ja2019/07/27 15:48:24321052.3753.5852.57-------NMTNoTransformer(Big) with relative position, sentence-wise smooth, deep transformer, back translation, ensemble of 7 models.
2KNU_Hyundaizh-ja2019/07/27 10:30:04317950.0250.8450.23-------NMTNoTransformer(base) + *Used ASPEC ja-en corpus* with relative position, bt, multi source, r2l rerank, 6-model ensemble
3srcbzh-ja2019/07/25 11:37:44291749.8350.8950.00-------NMTNoTransformer (Big) with relative position, layer attention, sentence-wise smooth.
4NICT-5zh-ja2018/09/10 14:14:05226749.7950.6649.89----- 0.00 0.00NMTNoMLNMT
5NICT-5zh-ja2018/08/27 14:40:35216949.6750.4649.79----- 0.00 0.00NMTNoCombining En-Ja corpus with Zh-Ja as a multilingual model. *ADDITIONAL ASPEC CORPUS USED*
6Kyoto-Uzh-ja2017/07/29 08:02:07157748.4348.8448.51---- 0.00 0.00 0.00NMTNoEnsemble of 5 Shared BPE 40k
7NICT-5zh-ja2018/08/22 18:51:44205248.4348.7848.52----- 0.00 0.00NMTNoMixed fine tuning by first pretraining on En-Ja ASPEC data and then continue on the En-Ja+Zh-Ja data. Transformer.
8Kyoto-Uzh-ja2017/08/01 14:14:49172048.3448.7648.40---- 0.00 0.00 0.00NMTNoEnsemble of 7 shared BPE, averaged
9ORGANIZERzh-ja2017/08/02 09:59:33174046.8747.3047.00---- 0.00 0.00 0.00NMTNoGoogle's "Attention Is All You Need"
10NICT-2zh-ja2017/07/26 14:08:45148146.8447.5147.27---- 0.00 0.00 0.00NMTNoNMT 6 Ensembles * Bi-directional Reranking
11Kyoto-Uzh-ja2017/07/31 15:27:21164346.7447.7946.67---- 0.00 0.00 0.00NMTNoKW replacement without KW in the test set, BPE, 6 ensemble
12Kyoto-Uzh-ja2016/10/11 10:46:03132446.3647.0246.50---- 0.00 0.00 0.00NMTNovoc: 32k ensemble of 4 independent model + Chinese short unit
13Kyoto-Uzh-ja2016/08/20 22:50:33125646.0446.7046.05---- 0.00 0.00 0.00NMTNovoc: 30k ensemble of 3 independent model + reverse rescoring
14Kyoto-Uzh-ja2016/08/20 22:48:16125544.2945.0544.32---- 0.00 0.00 0.00NMTNosrc: 200k tgt: 50k 2-layers self-ensembling
15NICT-2zh-ja2017/07/26 13:58:44147744.2644.9044.50---- 0.00 0.00 0.00NMTNoNMT Single Model: BPE50k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
16ORGANIZERzh-ja2018/08/14 11:33:03190243.3143.5343.34----- 0.00 0.00NMTNoNMT with Attention
17NAISTzh-ja2015/08/31 08:23:3083441.7542.9541.93--- 0.00 0.00 0.00 0.00SMTNoTravatar System with NeuralMT Reranking
18UT-KAYzh-ja2016/08/20 07:12:52122140.5041.8140.67---- 0.00 0.00 0.00NMTNoEnsemble of our NMT models with and without domain adaptation
19NAISTzh-ja2014/08/01 17:33:0112440.2140.8240.15--- 0.00 0.00 0.00 0.00SMTNoTravatar-based Forest-to-String SMT System (Tuned BLEU+RIBES)
20NAISTzh-ja2014/07/31 11:42:3112040.1141.2940.30--- 0.00 0.00 0.00 0.00SMTNoTravatar-based Forest-to-String SMT System
21NICT-2zh-ja2016/08/05 18:05:03109940.0240.4540.29---- 0.00 0.00 0.00SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC) + Google 5-gram LM
22EHRzh-ja2016/07/31 17:06:57106339.7539.8539.40---- 0.00 0.00 0.00SMTYesLM-based merging of outputs of preordered word-based PBSMT(DL=6) and preordered character-based PBSMT(DL=6).
23EHRzh-ja2015/09/02 17:00:1686739.4339.9839.58--- 0.00 0.00 0.00 0.00SMTNoPhrase based SMT with preordering.
24NAISTzh-ja2015/08/31 08:26:3183539.3640.5139.47--- 0.00 0.00 0.00 0.00SMTNoTravatar System Baseline
25bjtu_nlpzh-ja2016/08/12 12:50:38113838.8339.2538.68---- 0.00 0.00 0.00NMTNoRNN Encoder-Decoder with attention mechanism, single model
26Kyoto-Uzh-ja2015/08/31 22:39:3684538.5339.4138.66--- 0.00 0.00 0.00 0.00EBMTNoKyotoEBMT system with bilingual RNNLM reranking
27EHRzh-ja2015/08/19 11:23:3672037.9038.6837.98--- 0.00 0.00 0.00 0.00SMT and RBMTYesSystem combination of RBMT with user dictionary plus SPE and phrase based SMT with preordering. Candidate selection by language model score.
28UT-KAYzh-ja2016/08/20 07:09:54122037.6339.0737.82---- 0.00 0.00 0.00NMTNoAn end-to-end NMT with 512 dimensional single-layer LSTMs, UNK replacement, and domain adaptation
29TOSHIBAzh-ja2015/07/23 15:14:5350837.4737.4437.34--- 0.00 0.00 0.00 0.00SMT and RBMTYesSystem combination SMT and RBMT(SPE) with RNNLM language model
30SAS_MTzh-ja2014/09/01 10:38:1326337.4237.6537.07--- 0.00 0.00 0.00 0.00SMTNoSyntactic reordering Hierarchical SMT (using SAS token tool)
31Kyoto-Uzh-ja2015/08/07 13:24:5559737.3038.2637.45--- 0.00 0.00 0.00 0.00EBMTNoUpdated JUMAN and added one reordering feature, w/ reranking
32Kyoto-Uzh-ja2015/07/17 09:04:2249136.7637.8236.94--- 0.00 0.00 0.00 0.00EBMTNoWAT2015 baseline with reranking
33Kyoto-Uzh-ja2016/08/07 18:28:23111036.6337.5436.70---- 0.00 0.00 0.00EBMTNoKyotoEBMT 2016 w/o reranking
34SAS_MTzh-ja2014/08/29 15:33:0723236.5836.2236.10--- 0.00 0.00 0.00 0.00SMTNoSyntactic reordering phrase-based SMT (SAS token tool)
35ORGANIZERzh-ja2014/07/11 20:04:101336.5237.0736.64--- 0.00 0.00 0.00 0.00SMTNoTree-to-String SMT (2014)
36ORGANIZERzh-ja2015/09/10 14:00:3387936.5237.0736.64--- 0.00 0.00 0.00 0.00SMTNoTree-to-String SMT (2015)
37Kyoto-Uzh-ja2015/08/31 22:38:2284436.3037.2236.44--- 0.00 0.00 0.00 0.00EBMTNoKyotoEBMT system without reranking
38TOSHIBAzh-ja2015/07/28 16:27:3252535.8536.0235.73--- 0.00 0.00 0.00 0.00SMT and RBMTYesRBMT with SPE(Statistical Post Editing) system
39Kyoto-Uzh-ja2015/07/17 09:01:4249035.6636.7135.81--- 0.00 0.00 0.00 0.00EBMTNoWAT2015 baseline
40EHRzh-ja2015/09/04 11:44:2686835.5935.5635.37--- 0.00 0.00 0.00 0.00SMT and RBMTYesRBMT with user dictionary plus SPE.
41ORGANIZERzh-ja2014/07/11 19:47:27435.4335.9135.64--- 0.00 0.00 0.00 0.00SMTNoHierarchical Phrase-based SMT (2014)
42Kyoto-Uzh-ja2014/09/01 21:33:2326834.7535.8934.83--- 0.00 0.00 0.00 0.00EBMTNoOur new baseline system after several modifications + 20-best parses, KN7, RNNLM reranking
43BJTUNLPzh-ja2015/08/25 14:55:2076934.7234.8734.79--- 0.00 0.00 0.00 0.00SMTNo
44BJTUNLPzh-ja2015/09/01 21:08:1086234.7234.8734.79--- 0.00 0.00 0.00 0.00SMTNoa dependency-to-string model for SMT
45ORGANIZERzh-ja2014/07/11 19:54:58834.6535.1634.77--- 0.00 0.00 0.00 0.00SMTNoPhrase-based SMT
46Sensezh-ja2014/08/26 15:17:4920033.6633.8633.46--- 0.00 0.00 0.00 0.00SMTNoCharacter based SMT
47Kyoto-Uzh-ja2014/08/31 23:42:4125833.5734.4333.45--- 0.00 0.00 0.00 0.00EBMTNoOur new baseline system after several modifications.
48EIWAzh-ja2014/08/20 11:56:0013833.5333.7433.87--- 0.00 0.00 0.00 0.00SMT and RBMTYesRBMT with user dictionary plus SPE(statistical post editing)
49Kyoto-Uzh-ja2014/08/19 09:31:0813333.2635.0933.62--- 0.00 0.00 0.00 0.00EBMTNoUsing n-best parses and RNNLM.
50Kyoto-Uzh-ja2014/08/19 10:21:3713532.6833.3032.45--- 0.00 0.00 0.00 0.00EBMTNoOur baseline system.
51WASUIPSzh-ja2014/09/17 12:00:4638832.6532.8132.59--- 0.00 0.00 0.00 0.00SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: stanford-ctb and juman, moses: 2.1.1).
52WASUIPSzh-ja2014/09/17 11:03:4638732.5232.6932.47--- 0.00 0.00 0.00 0.00SMTNoOur baseline system (segmentation tools: stanford-ctb and juman, moses: 2.1.1).
53WASUIPSzh-ja2014/09/17 10:26:4338432.4333.3632.48--- 0.00 0.00 0.00 0.00SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: kytea, moses: 2.1.1).
54WASUIPSzh-ja2014/09/17 01:05:3837532.1932.5532.54--- 0.00 0.00 0.00 0.00SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: urheen and mecab, moses: 2.1.1).
55WASUIPSzh-ja2014/09/17 10:24:5038332.0833.0932.18--- 0.00 0.00 0.00 0.00SMTNoOur baseline system (segmentation tools: kytea, moses: 2.1.1).
56WASUIPSzh-ja2014/09/17 01:03:5737431.8732.2632.26--- 0.00 0.00 0.00 0.00SMTNoOur baseline system (segmentation tools: urheen and mecab, moses: 2.1.1).
57WASUIPSzh-ja2014/09/17 00:46:0737030.4430.9230.86--- 0.00 0.00 0.00 0.00SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: urheen and mecab, moses: 1.0).
58Sensezh-ja2015/07/29 07:20:2053329.2930.5229.45--- 0.00 0.00 0.00 0.00SMTNoBaseline-2015
59WASUIPSzh-ja2014/09/17 10:10:4738027.8628.8928.00--- 0.00 0.00 0.00 0.00SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: kytea, moses: 1.0).
60WASUIPSzh-ja2014/09/17 00:43:3836927.6628.0928.20--- 0.00 0.00 0.00 0.00SMTNoOur baseline system (segmentation tools: urheen and mecab, moses: 1.0).
61WASUIPSzh-ja2014/09/17 10:07:4437927.3728.2827.43--- 0.00 0.00 0.00 0.00SMTNoOur baseline system (segmentation tools: kytea, moses: 1.0).
62JAPIOzh-ja2016/08/19 16:44:49120826.2427.8726.37---- 0.00 0.00 0.00SMTYesPhrase-based SMT with Preordering + JAPIO corpus + rule-based posteditor
63TOSHIBAzh-ja2015/08/17 12:11:5266919.2419.4819.12--- 0.00 0.00 0.00 0.00RBMTYesRBMT
64ORGANIZERzh-ja2016/11/16 11:28:00134218.7520.6419.04---- 0.00 0.00 0.00NMTYesOnline A (2016/11/14)
65EIWAzh-ja2014/08/20 11:52:4513718.6918.3318.32--- 0.00 0.00 0.00 0.00RBMTYesRBMT plus user dictionary
66ORGANIZERzh-ja2014/07/18 11:09:123611.6313.2111.87--- 0.00 0.00 0.00 0.00OtherYesOnline A (2014)
67ORGANIZERzh-ja2016/07/26 11:54:14104311.5612.8711.69---- 0.00 0.00 0.00OtherYesOnline A (2016)
68ORGANIZERzh-ja2015/08/25 18:58:0877611.5312.8211.68--- 0.00 0.00 0.00 0.00OtherYesOnline A (2015)
69ORGANIZERzh-ja2014/08/28 12:10:1321510.4811.2610.47--- 0.00 0.00 0.00 0.00OtherYesOnline B (2014)
70ORGANIZERzh-ja2015/09/11 10:09:2389010.4111.0310.36--- 0.00 0.00 0.00 0.00OtherYesOnline B (2015)
71ORGANIZERzh-ja2014/08/29 18:45:03239 9.37 9.87 9.35--- 0.00 0.00 0.00 0.00RBMTNoRBMT A (2014)
72ORGANIZERzh-ja2015/09/10 14:30:56885 9.37 9.87 9.35--- 0.00 0.00 0.00 0.00OtherYesRBMT A (2015)
73ORGANIZERzh-ja2014/08/29 18:48:29242 8.39 8.70 8.30--- 0.00 0.00 0.00 0.00RBMTNoRBMT D
74TMUzh-ja2018/09/14 17:30:332343 6.21 7.02 6.27----- 0.00 0.00NMTYesUnsupervised NMT with sub-character information. Both ASPEC and JPC 4.0 data (zh-ja) were also used as monolingual data in the training.

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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
1srcbzh-ja2019/07/27 15:48:2432100.8952310.8918720.895663-------NMTNoTransformer(Big) with relative position, sentence-wise smooth, deep transformer, back translation, ensemble of 7 models.
2NICT-5zh-ja2018/09/10 14:14:0522670.8896740.8864900.889853-----0.0000000.000000NMTNoMLNMT
3KNU_Hyundaizh-ja2019/07/27 10:30:0431790.8885300.8862480.888706-------NMTNoTransformer(base) + *Used ASPEC ja-en corpus* with relative position, bt, multi source, r2l rerank, 6-model ensemble
4NICT-5zh-ja2018/08/27 14:40:3521690.8861610.8829890.886367-----0.0000000.000000NMTNoCombining En-Ja corpus with Zh-Ja as a multilingual model. *ADDITIONAL ASPEC CORPUS USED*
5srcbzh-ja2019/07/25 11:37:4429170.8860690.8827120.886854-------NMTNoTransformer (Big) with relative position, layer attention, sentence-wise smooth.
6NICT-5zh-ja2018/08/22 18:51:4420520.8844260.8794560.884782-----0.0000000.000000NMTNoMixed fine tuning by first pretraining on En-Ja ASPEC data and then continue on the En-Ja+Zh-Ja data. Transformer.
7Kyoto-Uzh-ja2017/08/01 14:14:4917200.8842100.8800690.884745----0.0000000.0000000.000000NMTNoEnsemble of 7 shared BPE, averaged
8Kyoto-Uzh-ja2017/07/29 08:02:0715770.8834570.8789640.884137----0.0000000.0000000.000000NMTNoEnsemble of 5 Shared BPE 40k
9NICT-2zh-ja2017/07/26 14:08:4514810.8823560.8785800.882195----0.0000000.0000000.000000NMTNoNMT 6 Ensembles * Bi-directional Reranking
10ORGANIZERzh-ja2017/08/02 09:59:3317400.8808150.8755110.880368----0.0000000.0000000.000000NMTNoGoogle's "Attention Is All You Need"
11Kyoto-Uzh-ja2017/07/31 15:27:2116430.8780080.8729440.878627----0.0000000.0000000.000000NMTNoKW replacement without KW in the test set, BPE, 6 ensemble
12Kyoto-Uzh-ja2016/08/20 22:50:3312560.8765310.8729040.876946----0.0000000.0000000.000000NMTNovoc: 30k ensemble of 3 independent model + reverse rescoring
13Kyoto-Uzh-ja2016/10/11 10:46:0313240.8752790.8701750.875564----0.0000000.0000000.000000NMTNovoc: 32k ensemble of 4 independent model + Chinese short unit
14NICT-2zh-ja2017/07/26 13:58:4414770.8714380.8683590.871736----0.0000000.0000000.000000NMTNoNMT Single Model: BPE50k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
15ORGANIZERzh-ja2018/08/14 11:33:0319020.8707340.8662810.870886-----0.0000000.000000NMTNoNMT with Attention
16Kyoto-Uzh-ja2016/08/20 22:48:1612550.8693600.8647480.869913----0.0000000.0000000.000000NMTNosrc: 200k tgt: 50k 2-layers self-ensembling
17UT-KAYzh-ja2016/08/20 07:12:5212210.8602140.8546900.860449----0.0000000.0000000.000000NMTNoEnsemble of our NMT models with and without domain adaptation
18NAISTzh-ja2015/08/31 08:23:308340.8550890.8477460.854587---0.0000000.0000000.0000000.000000SMTNoTravatar System with NeuralMT Reranking
19bjtu_nlpzh-ja2016/08/12 12:50:3811380.8528180.8463010.852298----0.0000000.0000000.000000NMTNoRNN Encoder-Decoder with attention mechanism, single model
20UT-KAYzh-ja2016/08/20 07:09:5412200.8474070.8420550.848040----0.0000000.0000000.000000NMTNoAn end-to-end NMT with 512 dimensional single-layer LSTMs, UNK replacement, and domain adaptation
21NAISTzh-ja2014/08/01 17:33:011240.8454860.8380920.845625---0.0000000.0000000.0000000.000000SMTNoTravatar-based Forest-to-String SMT System (Tuned BLEU+RIBES)
22NICT-2zh-ja2016/08/05 18:05:0310990.8439410.8377070.842513----0.0000000.0000000.000000SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC) + Google 5-gram LM
23EHRzh-ja2016/07/31 17:06:5710630.8437230.8361560.841952----0.0000000.0000000.000000SMTYesLM-based merging of outputs of preordered word-based PBSMT(DL=6) and preordered character-based PBSMT(DL=6).
24NAISTzh-ja2014/07/31 11:42:311200.8424770.8348240.842235---0.0000000.0000000.0000000.000000SMTNoTravatar-based Forest-to-String SMT System
25Kyoto-Uzh-ja2015/08/31 22:39:368450.8406810.8344510.839063---0.0000000.0000000.0000000.000000EBMTNoKyotoEBMT system with bilingual RNNLM reranking
26EHRzh-ja2015/09/02 17:00:168670.8376780.8316820.837227---0.0000000.0000000.0000000.000000SMTNoPhrase based SMT with preordering.
27NAISTzh-ja2015/08/31 08:26:318350.8343880.8271480.834130---0.0000000.0000000.0000000.000000SMTNoTravatar System Baseline
28SAS_MTzh-ja2014/09/01 10:38:132630.8341700.8255510.833048---0.0000000.0000000.0000000.000000SMTNoSyntactic reordering Hierarchical SMT (using SAS token tool)
29TOSHIBAzh-ja2015/07/23 15:14:535080.8272910.8173950.825472---0.0000000.0000000.0000000.000000SMT and RBMTYesSystem combination SMT and RBMT(SPE) with RNNLM language model
30EHRzh-ja2015/08/19 11:23:367200.8260030.8186200.824806---0.0000000.0000000.0000000.000000SMT and RBMTYesSystem combination of RBMT with user dictionary plus SPE and phrase based SMT with preordering. Candidate selection by language model score.
31ORGANIZERzh-ja2014/07/11 20:04:10130.8252920.8204900.825025---0.0000000.0000000.0000000.000000SMTNoTree-to-String SMT (2014)
32ORGANIZERzh-ja2015/09/10 14:00:338790.8252920.8204900.825025---0.0000000.0000000.0000000.000000SMTNoTree-to-String SMT (2015)
33TOSHIBAzh-ja2015/07/28 16:27:325250.8247400.8153880.822423---0.0000000.0000000.0000000.000000SMT and RBMTYesRBMT with SPE(Statistical Post Editing) system
34Kyoto-Uzh-ja2015/08/07 13:24:555970.8226720.8170370.822340---0.0000000.0000000.0000000.000000EBMTNoUpdated JUMAN and added one reordering feature, w/ reranking
35SAS_MTzh-ja2014/08/29 15:33:072320.8221800.8075350.817368---0.0000000.0000000.0000000.000000SMTNoSyntactic reordering phrase-based SMT (SAS token tool)
36Kyoto-Uzh-ja2016/08/07 18:28:2311100.8202590.8146610.819963----0.0000000.0000000.000000EBMTNoKyotoEBMT 2016 w/o reranking
37Kyoto-Uzh-ja2015/08/31 22:38:228440.8197430.8145810.818794---0.0000000.0000000.0000000.000000EBMTNoKyotoEBMT system without reranking
38Kyoto-Uzh-ja2015/07/17 09:04:224910.8184450.8129100.817522---0.0000000.0000000.0000000.000000EBMTNoWAT2015 baseline with reranking
39EHRzh-ja2015/09/04 11:44:268680.8158420.8067260.813996---0.0000000.0000000.0000000.000000SMT and RBMTYesRBMT with user dictionary plus SPE.
40EIWAzh-ja2014/08/20 11:56:001380.8113500.8005060.808504---0.0000000.0000000.0000000.000000SMT and RBMTYesRBMT with user dictionary plus SPE(statistical post editing)
41ORGANIZERzh-ja2014/07/11 19:47:2740.8104060.7987260.807665---0.0000000.0000000.0000000.000000SMTNoHierarchical Phrase-based SMT (2014)
42Kyoto-Uzh-ja2015/07/17 09:01:424900.8093950.8037800.808692---0.0000000.0000000.0000000.000000EBMTNoWAT2015 baseline
43BJTUNLPzh-ja2015/08/25 14:55:207690.8070120.7924880.802430---0.0000000.0000000.0000000.000000SMTNo
44BJTUNLPzh-ja2015/09/01 21:08:108620.8070120.7924880.802430---0.0000000.0000000.0000000.000000SMTNoa dependency-to-string model for SMT
45Kyoto-Uzh-ja2014/09/01 21:33:232680.8026290.7986310.802930---0.0000000.0000000.0000000.000000EBMTNoOur new baseline system after several modifications + 20-best parses, KN7, RNNLM reranking
46Kyoto-Uzh-ja2014/08/31 23:42:412580.8009490.7953900.800986---0.0000000.0000000.0000000.000000EBMTNoOur new baseline system after several modifications.
47WASUIPSzh-ja2014/09/17 12:00:463880.7967770.7817330.791219---0.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: stanford-ctb and juman, moses: 2.1.1).
48WASUIPSzh-ja2014/09/17 10:26:433840.7962200.7780750.789657---0.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: kytea, moses: 2.1.1).
49WASUIPSzh-ja2014/09/17 11:03:463870.7960590.7804020.790107---0.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: stanford-ctb and juman, moses: 2.1.1).
50WASUIPSzh-ja2014/09/17 01:05:383750.7958380.7800270.787591---0.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: urheen and mecab, moses: 2.1.1).
51WASUIPSzh-ja2014/09/17 01:03:573740.7943030.7778760.786422---0.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: urheen and mecab, moses: 2.1.1).
52WASUIPSzh-ja2014/09/17 10:24:503830.7932300.7751680.787665---0.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: kytea, moses: 2.1.1).
53Kyoto-Uzh-ja2014/08/19 09:31:081330.7916800.7871050.791269---0.0000000.0000000.0000000.000000EBMTNoUsing n-best parses and RNNLM.
54JAPIOzh-ja2016/08/19 16:44:4912080.7905530.7806370.785917----0.0000000.0000000.000000SMTYesPhrase-based SMT with Preordering + JAPIO corpus + rule-based posteditor
55WASUIPSzh-ja2014/09/17 00:46:073700.7898240.7731420.781475---0.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: urheen and mecab, moses: 1.0).
56Sensezh-ja2014/08/26 15:17:492000.7894950.7743380.784012---0.0000000.0000000.0000000.000000SMTNoCharacter based SMT
57Kyoto-Uzh-ja2014/08/19 10:21:371350.7862290.7830160.786352---0.0000000.0000000.0000000.000000EBMTNoOur baseline system.
58WASUIPSzh-ja2014/09/17 00:43:383690.7791830.7629490.770846---0.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: urheen and mecab, moses: 1.0).
59WASUIPSzh-ja2014/09/17 10:10:473800.7765500.7567210.769409---0.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: kytea, moses: 1.0).
60Sensezh-ja2015/07/29 07:20:205330.7746920.7648470.772410---0.0000000.0000000.0000000.000000SMTNoBaseline-2015
61WASUIPSzh-ja2014/09/17 10:07:443790.7744230.7537490.767073---0.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: kytea, moses: 1.0).
62ORGANIZERzh-ja2014/07/11 19:54:5880.7724980.7663840.771005---0.0000000.0000000.0000000.000000SMTNoPhrase-based SMT
63TOSHIBAzh-ja2015/08/17 12:11:526690.7416650.7271550.738298---0.0000000.0000000.0000000.000000RBMTYesRBMT
64EIWAzh-ja2014/08/20 11:52:451370.7401830.7202810.732466---0.0000000.0000000.0000000.000000RBMTYesRBMT plus user dictionary
65ORGANIZERzh-ja2016/11/16 11:28:0013420.7190220.7171730.720095----0.0000000.0000000.000000NMTYesOnline A (2016/11/14)
66ORGANIZERzh-ja2014/08/29 18:45:032390.6662770.6524020.661730---0.0000000.0000000.0000000.000000RBMTNoRBMT A (2014)
67ORGANIZERzh-ja2015/09/10 14:30:568850.6662770.6524020.661730---0.0000000.0000000.0000000.000000OtherYesRBMT A (2015)
68ORGANIZERzh-ja2014/08/29 18:48:292420.6411890.6264000.633319---0.0000000.0000000.0000000.000000RBMTNoRBMT D
69ORGANIZERzh-ja2014/08/28 12:10:132150.6007330.5960060.600706---0.0000000.0000000.0000000.000000OtherYesOnline B (2014)
70ORGANIZERzh-ja2015/09/11 10:09:238900.5973550.5928410.597298---0.0000000.0000000.0000000.000000OtherYesOnline B (2015)
71ORGANIZERzh-ja2014/07/18 11:09:12360.5959250.5981720.598573---0.0000000.0000000.0000000.000000OtherYesOnline A (2014)
72ORGANIZERzh-ja2016/07/26 11:54:1410430.5898020.5893970.593361----0.0000000.0000000.000000OtherYesOnline A (2016)
73ORGANIZERzh-ja2015/08/25 18:58:087760.5882850.5903930.592887---0.0000000.0000000.0000000.000000OtherYesOnline A (2015)
74TMUzh-ja2018/09/14 17:30:3323430.5700400.5623310.565795-----0.0000000.000000NMTYesUnsupervised NMT with sub-character information. Both ASPEC and JPC 4.0 data (zh-ja) were also used as monolingual data in the training.

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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
1srcbzh-ja2019/07/27 15:48:2432100.8190200.8190200.819020-------NMTNoTransformer(Big) with relative position, sentence-wise smooth, deep transformer, back translation, ensemble of 7 models.
2srcbzh-ja2019/07/25 11:37:4429170.8124500.8124500.812450-------NMTNoTransformer (Big) with relative position, layer attention, sentence-wise smooth.
3KNU_Hyundaizh-ja2019/07/27 10:30:0431790.8099900.8099900.809990-------NMTNoTransformer(base) + *Used ASPEC ja-en corpus* with relative position, bt, multi source, r2l rerank, 6-model ensemble
4NICT-5zh-ja2018/08/27 14:40:3521690.8057500.8057500.805750-----0.0000000.000000NMTNoCombining En-Ja corpus with Zh-Ja as a multilingual model. *ADDITIONAL ASPEC CORPUS USED*
5NICT-5zh-ja2018/09/10 14:14:0522670.8049200.8049200.804920-----0.0000000.000000NMTNoMLNMT
6NICT-5zh-ja2018/08/22 18:51:4420520.8006700.8006700.800670-----0.0000000.000000NMTNoMixed fine tuning by first pretraining on En-Ja ASPEC data and then continue on the En-Ja+Zh-Ja data. Transformer.
7Kyoto-Uzh-ja2017/08/01 14:14:4917200.7998400.7998400.799840----0.0000000.0000000.000000NMTNoEnsemble of 7 shared BPE, averaged
8NICT-2zh-ja2017/07/26 14:08:4514810.7996800.7996800.799680----0.0000000.0000000.000000NMTNoNMT 6 Ensembles * Bi-directional Reranking
9Kyoto-Uzh-ja2017/07/29 08:02:0715770.7995200.7995200.799520----0.0000000.0000000.000000NMTNoEnsemble of 5 Shared BPE 40k
10ORGANIZERzh-ja2017/08/02 09:59:3317400.7981100.7981100.798110----0.0000000.0000000.000000NMTNoGoogle's "Attention Is All You Need"
11Kyoto-Uzh-ja2017/07/31 15:27:2116430.7934100.7934100.793410----0.0000000.0000000.000000NMTNoKW replacement without KW in the test set, BPE, 6 ensemble
12NICT-2zh-ja2017/07/26 13:58:4414770.7889400.7889400.788940----0.0000000.0000000.000000NMTNoNMT Single Model: BPE50k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder
13Kyoto-Uzh-ja2016/10/11 10:46:0313240.7879300.7879300.787930----0.0000000.0000000.000000NMTNovoc: 32k ensemble of 4 independent model + Chinese short unit
14Kyoto-Uzh-ja2016/08/20 22:50:3312560.7859100.7859100.785910----0.0000000.0000000.000000NMTNovoc: 30k ensemble of 3 independent model + reverse rescoring
15Kyoto-Uzh-ja2016/08/20 22:48:1612550.7843800.7843800.784380----0.0000000.0000000.000000NMTNosrc: 200k tgt: 50k 2-layers self-ensembling
16ORGANIZERzh-ja2018/08/14 11:33:0319020.7821000.7821000.782100-----0.0000000.000000NMTNoNMT with Attention
17NAISTzh-ja2015/08/31 08:23:308340.7710100.7710100.7710100.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoTravatar System with NeuralMT Reranking
18Kyoto-Uzh-ja2015/08/31 22:39:368450.7697000.7697000.7697000.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoKyotoEBMT system with bilingual RNNLM reranking
19EHRzh-ja2016/07/31 17:06:5710630.7694900.7694900.769490----0.0000000.0000000.000000SMTYesLM-based merging of outputs of preordered word-based PBSMT(DL=6) and preordered character-based PBSMT(DL=6).
20NICT-2zh-ja2016/08/05 18:05:0310990.7685800.7685800.768580----0.0000000.0000000.000000SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC) + Google 5-gram LM
21NAISTzh-ja2014/07/31 11:42:311200.7681900.7681900.7681900.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoTravatar-based Forest-to-String SMT System
22Kyoto-Uzh-ja2016/08/07 18:28:2311100.7671200.7671200.767120----0.0000000.0000000.000000EBMTNoKyotoEBMT 2016 w/o reranking
23NAISTzh-ja2014/08/01 17:33:011240.7662700.7662700.7662700.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoTravatar-based Forest-to-String SMT System (Tuned BLEU+RIBES)
24SAS_MTzh-ja2014/09/01 10:38:132630.7657300.7657300.7657300.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoSyntactic reordering Hierarchical SMT (using SAS token tool)
25UT-KAYzh-ja2016/08/20 07:12:5212210.7655300.7655300.765530----0.0000000.0000000.000000NMTNoEnsemble of our NMT models with and without domain adaptation
26EHRzh-ja2015/08/19 11:23:367200.7650500.7650500.7650500.0000000.0000000.0000000.0000000.0000000.0000000.000000SMT and RBMTYesSystem combination of RBMT with user dictionary plus SPE and phrase based SMT with preordering. Candidate selection by language model score.
27NAISTzh-ja2015/08/31 08:26:318350.7648300.7648300.7648300.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoTravatar System Baseline
28Kyoto-Uzh-ja2015/08/07 13:24:555970.7624300.7624300.7624300.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoUpdated JUMAN and added one reordering feature, w/ reranking
29Kyoto-Uzh-ja2015/07/17 09:04:224910.7621800.7621800.7621800.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoWAT2015 baseline with reranking
30Kyoto-Uzh-ja2015/08/31 22:38:228440.7619600.7619600.7619600.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoKyotoEBMT system without reranking
31bjtu_nlpzh-ja2016/08/12 12:50:3811380.7608400.7608400.760840----0.0000000.0000000.000000NMTNoRNN Encoder-Decoder with attention mechanism, single model
32TOSHIBAzh-ja2015/07/28 16:27:325250.7581100.7581100.7581100.0000000.0000000.0000000.0000000.0000000.0000000.000000SMT and RBMTYesRBMT with SPE(Statistical Post Editing) system
33Kyoto-Uzh-ja2014/09/01 21:33:232680.7576100.7576100.7576100.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoOur new baseline system after several modifications + 20-best parses, KN7, RNNLM reranking
34Kyoto-Uzh-ja2015/07/17 09:01:424900.7570700.7570700.7570700.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoWAT2015 baseline
35ORGANIZERzh-ja2014/07/11 20:04:10130.7548700.7548700.7548700.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoTree-to-String SMT (2014)
36ORGANIZERzh-ja2015/09/10 14:00:338790.7548700.7548700.7548700.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoTree-to-String SMT (2015)
37EHRzh-ja2015/09/04 11:44:268680.7541800.7541800.7541800.0000000.0000000.0000000.0000000.0000000.0000000.000000SMT and RBMTYesRBMT with user dictionary plus SPE.
38UT-KAYzh-ja2016/08/20 07:09:5412200.7538200.7538200.753820----0.0000000.0000000.000000NMTNoAn end-to-end NMT with 512 dimensional single-layer LSTMs, UNK replacement, and domain adaptation
39WASUIPSzh-ja2014/09/17 10:24:503830.7537500.7537500.7537500.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: kytea, moses: 2.1.1).
40WASUIPSzh-ja2014/09/17 10:26:433840.7536900.7536900.7536900.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: kytea, moses: 2.1.1).
41ORGANIZERzh-ja2014/07/11 19:54:5880.7530100.7530100.7530100.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoPhrase-based SMT
42Sensezh-ja2014/08/26 15:17:492000.7528900.7528900.7528900.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoCharacter based SMT
43TOSHIBAzh-ja2015/07/23 15:14:535080.7528300.7528300.7528300.0000000.0000000.0000000.0000000.0000000.0000000.000000SMT and RBMTYesSystem combination SMT and RBMT(SPE) with RNNLM language model
44SAS_MTzh-ja2014/08/29 15:33:072320.7521700.7521700.7521700.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoSyntactic reordering phrase-based SMT (SAS token tool)
45ORGANIZERzh-ja2014/07/11 19:47:2740.7509500.7509500.7509500.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoHierarchical Phrase-based SMT (2014)
46Kyoto-Uzh-ja2014/08/31 23:42:412580.7503700.7503700.7503700.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoOur new baseline system after several modifications.
47Kyoto-Uzh-ja2014/08/19 09:31:081330.7503100.7503100.7503100.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoUsing n-best parses and RNNLM.
48Kyoto-Uzh-ja2014/08/19 10:21:371350.7482000.7482000.7482000.0000000.0000000.0000000.0000000.0000000.0000000.000000EBMTNoOur baseline system.
49BJTUNLPzh-ja2015/08/25 14:55:207690.7441300.7441300.7441300.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNo
50BJTUNLPzh-ja2015/09/01 21:08:108620.7441300.7441300.7441300.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoa dependency-to-string model for SMT
51WASUIPSzh-ja2014/09/17 12:00:463880.7440400.7440400.7440400.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: stanford-ctb and juman, moses: 2.1.1).
52WASUIPSzh-ja2014/09/17 11:03:463870.7431400.7431400.7431400.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: stanford-ctb and juman, moses: 2.1.1).
53WASUIPSzh-ja2014/09/17 01:03:573740.7406500.7406500.7406500.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: urheen and mecab, moses: 2.1.1).
54WASUIPSzh-ja2014/09/17 01:05:383750.7406400.7406400.7406400.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: urheen and mecab, moses: 2.1.1).
55WASUIPSzh-ja2014/09/17 00:46:073700.7346200.7346200.7346200.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: urheen and mecab, moses: 1.0).
56Sensezh-ja2015/07/29 07:20:205330.7331900.7331900.7331900.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoBaseline-2015
57WASUIPSzh-ja2014/09/17 10:07:443790.7253600.7253600.7253600.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: kytea, moses: 1.0).
58WASUIPSzh-ja2014/09/17 10:10:473800.7252500.7252500.7252500.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTYesOur baseline system + additional quasi-parallel corpus (segmentation tools: kytea, moses: 1.0).
59WASUIPSzh-ja2014/09/17 00:43:383690.7116500.7116500.7116500.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoOur baseline system (segmentation tools: urheen and mecab, moses: 1.0).
60EHRzh-ja2015/09/02 17:00:168670.7073100.7073100.7073100.0000000.0000000.0000000.0000000.0000000.0000000.000000SMTNoPhrase based SMT with preordering.
61JAPIOzh-ja2016/08/19 16:44:4912080.6967700.6967700.696770----0.0000000.0000000.000000SMTYesPhrase-based SMT with Preordering + JAPIO corpus + rule-based posteditor
62EIWAzh-ja2014/08/20 11:56:001380.6933300.6933300.6933300.0000000.0000000.0000000.0000000.0000000.0000000.000000SMT and RBMTYesRBMT with user dictionary plus SPE(statistical post editing)
63ORGANIZERzh-ja2016/11/16 11:28:0013420.6928200.6928200.692820----0.0000000.0000000.000000NMTYesOnline A (2016/11/14)
64ORGANIZERzh-ja2016/07/26 11:54:1410430.6595400.6595400.659540----0.0000000.0000000.000000OtherYesOnline A (2016)
65ORGANIZERzh-ja2014/07/18 11:09:12360.6580600.6580600.6580600.0000000.0000000.0000000.0000000.0000000.0000000.000000OtherYesOnline A (2014)
66TOSHIBAzh-ja2015/08/17 12:11:526690.6540800.6540800.6540800.0000000.0000000.0000000.0000000.0000000.0000000.000000RBMTYesRBMT
67ORGANIZERzh-ja2015/08/25 18:58:087760.6498600.6498600.6498600.0000000.0000000.0000000.0000000.0000000.0000000.000000OtherYesOnline A (2015)
68ORGANIZERzh-ja2014/08/28 12:10:132150.6369300.6369300.6369300.0000000.0000000.0000000.0000000.0000000.0000000.000000OtherYesOnline B (2014)
69ORGANIZERzh-ja2015/09/11 10:09:238900.6282900.6282900.6282900.0000000.0000000.0000000.0000000.0000000.0000000.000000OtherYesOnline B (2015)
70ORGANIZERzh-ja2014/08/29 18:45:032390.6260700.6260700.6260700.0000000.0000000.0000000.0000000.0000000.0000000.000000RBMTNoRBMT A (2014)
71ORGANIZERzh-ja2015/09/10 14:30:568850.6260700.6260700.6260700.0000000.0000000.0000000.0000000.0000000.0000000.000000OtherYesRBMT A (2015)
72EIWAzh-ja2014/08/20 11:52:451370.6137300.6137300.6137300.0000000.0000000.0000000.0000000.0000000.0000000.000000RBMTYesRBMT plus user dictionary
73ORGANIZERzh-ja2014/08/29 18:48:292420.5867900.5867900.5867900.0000000.0000000.0000000.0000000.0000000.0000000.000000RBMTNoRBMT D
74TMUzh-ja2018/09/14 17:30:3323430.5124300.5124300.512430-----0.0000000.000000NMTYesUnsupervised NMT with sub-character information. Both ASPEC and JPC 4.0 data (zh-ja) were also used as monolingual data in the training.

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HUMAN (WAT2019)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1KNU_Hyundaizh-ja2019/07/27 10:30:043179UnderwayNMTNoTransformer(base) + *Used ASPEC ja-en corpus* with relative position, bt, multi source, r2l rerank, 6-model ensemble
2srcbzh-ja2019/07/27 15:48:243210UnderwayNMTNoTransformer(Big) with relative position, sentence-wise smooth, deep transformer, back translation, ensemble of 7 models.

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HUMAN (WAT2018)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1NICT-5zh-ja2018/09/10 14:14:05226722.750NMTNoMLNMT
2NICT-5zh-ja2018/08/22 18:51:44205211.000NMTNoMixed fine tuning by first pretraining on En-Ja ASPEC data and then continue on the En-Ja+Zh-Ja data. Transformer.

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HUMAN (WAT2017)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1Kyoto-Uzh-ja2017/08/01 14:14:49172082.750NMTNoEnsemble of 7 shared BPE, averaged
2Kyoto-Uzh-ja2017/07/29 08:02:07157779.500NMTNoEnsemble of 5 Shared BPE 40k
3NICT-2zh-ja2017/07/26 14:08:45148179.000NMTNoNMT 6 Ensembles * Bi-directional Reranking
4ORGANIZERzh-ja2017/08/02 09:59:33174078.500NMTNoGoogle's "Attention Is All You Need"
5NICT-2zh-ja2017/07/26 13:58:44147778.000NMTNoNMT Single Model: BPE50k, Bi-LSTM(500*2) Encoder, LSTM(1000) Left-to-Right Decoder

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HUMAN (WAT2016)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1Kyoto-Uzh-ja2016/08/20 22:50:33125663.750NMTNovoc: 30k ensemble of 3 independent model + reverse rescoring
2Kyoto-Uzh-ja2016/08/20 22:48:16125556.000NMTNosrc: 200k tgt: 50k 2-layers self-ensembling
3bjtu_nlpzh-ja2016/08/12 12:50:38113849.000NMTNoRNN Encoder-Decoder with attention mechanism, single model
4UT-KAYzh-ja2016/08/20 07:12:52122147.250NMTNoEnsemble of our NMT models with and without domain adaptation
5UT-KAYzh-ja2016/08/20 07:09:54122041.000NMTNoAn end-to-end NMT with 512 dimensional single-layer LSTMs, UNK replacement, and domain adaptation
6NICT-2zh-ja2016/08/05 18:05:03109936.500SMTYesPhrase-based SMT with Preordering + Domain Adaptation (JPC and ASPEC) + Google 5-gram LM
7EHRzh-ja2016/07/31 17:06:57106332.500SMTYesLM-based merging of outputs of preordered word-based PBSMT(DL=6) and preordered character-based PBSMT(DL=6).
8ORGANIZERzh-ja2016/11/16 11:28:00134222.500NMTYesOnline A (2016/11/14)
9JAPIOzh-ja2016/08/19 16:44:49120816.500SMTYesPhrase-based SMT with Preordering + JAPIO corpus + rule-based posteditor
10ORGANIZERzh-ja2016/07/26 11:54:141043-51.250OtherYesOnline A (2016)

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HUMAN (WAT2015)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1NAISTzh-ja2015/08/31 08:23:3083435.750SMTNoTravatar System with NeuralMT Reranking
2EHRzh-ja2015/08/19 11:23:3672025.750SMT and RBMTYesSystem combination of RBMT with user dictionary plus SPE and phrase based SMT with preordering. Candidate selection by language model score.
3NAISTzh-ja2015/08/31 08:26:3183525.750SMTNoTravatar System Baseline
4Kyoto-Uzh-ja2015/08/31 22:39:3684518.500EBMTNoKyotoEBMT system with bilingual RNNLM reranking
5TOSHIBAzh-ja2015/07/23 15:14:5350818.000SMT and RBMTYesSystem combination SMT and RBMT(SPE) with RNNLM language model
6ORGANIZERzh-ja2015/09/10 14:00:3387917.250SMTNoTree-to-String SMT (2015)
7Kyoto-Uzh-ja2015/08/31 22:38:2284416.750EBMTNoKyotoEBMT system without reranking
8BJTUNLPzh-ja2015/09/01 21:08:108626.500SMTNoa dependency-to-string model for SMT
9TOSHIBAzh-ja2015/07/28 16:27:32525-1.000SMT and RBMTYesRBMT with SPE(Statistical Post Editing) system
10ORGANIZERzh-ja2015/08/25 18:58:08776-19.000OtherYesOnline A (2015)
11ORGANIZERzh-ja2015/09/10 14:30:56885-28.000OtherYesRBMT A (2015)

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HUMAN (WAT2014)


# Team Task Date/Time DataID HUMAN
Method
Other
Resources
System
Description
1NAISTzh-ja2014/07/31 11:42:3112050.750SMTNoTravatar-based Forest-to-String SMT System
2NAISTzh-ja2014/08/01 17:33:0112438.000SMTNoTravatar-based Forest-to-String SMT System (Tuned BLEU+RIBES)
3SAS_MTzh-ja2014/09/01 10:38:1326322.500SMTNoSyntactic reordering Hierarchical SMT (using SAS token tool)
4ORGANIZERzh-ja2014/07/11 20:04:101316.000SMTNoTree-to-String SMT (2014)
5EIWAzh-ja2014/08/20 11:56:0013815.000SMT and RBMTYesRBMT with user dictionary plus SPE(statistical post editing)
6Kyoto-Uzh-ja2014/09/01 21:33:232687.500EBMTNoOur new baseline system after several modifications + 20-best parses, KN7, RNNLM reranking
7Kyoto-Uzh-ja2014/08/31 23:42:412586.000EBMTNoOur new baseline system after several modifications.
8ORGANIZERzh-ja2014/07/11 19:47:2744.750SMTNoHierarchical Phrase-based SMT (2014)
9Sensezh-ja2014/08/26 15:17:49200-1.000SMTNoCharacter based SMT
10ORGANIZERzh-ja2014/07/18 11:09:1236-21.750OtherYesOnline A (2014)
11ORGANIZERzh-ja2014/08/29 18:45:03239-37.750RBMTNoRBMT A (2014)

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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