WAT

The Workshop on Asian Translation

Evaluation Results

BLEU

#	Team	Task	Date/Time	DataID	BLEU										Method	Other Resources	System Description
#	Team	Task	Date/Time	DataID	juman	kytea	mecab	moses- tokenizer	stanford- segmenter- ctb	stanford- segmenter- pku	indic- tokenizer	unuse	myseg	kmseg	Method	Other Resources	System Description
1	sarah	JPCNen-ja	2019/07/22 14:59:33	2804	44.14	46.22	44.32	-	-	-	-	-	-	-	NMT	No	Transformer, single model
2	sarah	JPCNen-ja	2019/07/25 13:02:06	2926	44.97	47.11	45.21	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
3	ryan	JPCNen-ja	2019/07/26 08:51:18	2961	44.27	46.09	44.37	-	-	-	-	-	-	-	NMT	No	Base Transformer
4	KNU_Hyundai	JPCNen-ja	2019/07/27 11:59:37	3184	47.19	48.94	47.29	-	-	-	-	-	-	-	NMT	No	Transformer Base, relative position, BT, r2l reranking, checkpoint ensemble
5	KNU_Hyundai	JPCNen-ja	2019/07/27 12:48:32	3192	47.38	48.98	47.44	-	-	-	-	-	-	-	NMT	Yes	Transformer Base (+ASPEC data), relative position, BT, r2l reranking, checkpoint ensemble
6	goku20	JPCNen-ja	2020/09/18 17:20:54	3922	45.43	47.12	45.36	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 3 models
7	goku20	JPCNen-ja	2020/09/18 17:31:17	3929	44.52	46.63	44.57	-	-	-	-	-	-	-	NMT	No	mBART pre-training, ensemble of 3 models
8	TMU	JPCNen-ja	2021/04/22 07:34:06	5347	45.24	47.12	45.27	-	-	-	-	-	-	-	NMT	No	Japanese BART, ensemble of 3 models
9	tpt_wat	JPCNen-ja	2021/04/27 02:23:00	5705	46.83	48.52	46.98	-	-	-	-	-	-	-	NMT	No	Base Transformer model with joint vocab, size 8k
10	Bering Lab	JPCNen-ja	2021/05/04 20:56:31	6391	47.08	48.68	47.09	-	-	-	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
11	ORGANIZER	JPCNen-ja	2018/08/16 16:40:36	1999	41.26	43.13	41.16	-	-	-	-	-	0.00	0.00	NMT	No	NMT with Attention
12	EHR	JPCNen-ja	2018/09/08 20:24:32	2247	44.70	46.24	44.57	-	-	-	-	-	0.00	0.00	NMT	No	SMT reranked NMT
13	EHR	JPCNen-ja	2018/09/13 12:49:07	2283	44.71	46.13	44.63	-	-	-	-	-	0.00	0.00	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 13)
14	EHR	JPCNen-ja	2018/09/15 15:47:49	2394	45.27	46.70	45.13	-	-	-	-	-	0.00	0.00	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 18)
15	EHR	JPCNen-ja	2018/09/16 15:07:27	2475	45.39	46.84	45.27	-	-	-	-	-	0.00	0.00	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 20)

Notice:

This table is sorted by the leftmost segmenters. You can change the segmenter used to sort by clicking each segmenter link.

RIBES

#	Team	Task	Date/Time	DataID	RIBES										Method	Other Resources	System Description
#	Team	Task	Date/Time	DataID	juman	kytea	mecab	moses- tokenizer	stanford- segmenter- ctb	stanford- segmenter- pku	indic- tokenizer	unuse	myseg	kmseg	Method	Other Resources	System Description
1	sarah	JPCNen-ja	2019/07/22 14:59:33	2804	0.848681	0.848081	0.848761	-	-	-	-	-	-	-	NMT	No	Transformer, single model
2	sarah	JPCNen-ja	2019/07/25 13:02:06	2926	0.852016	0.851731	0.852252	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
3	ryan	JPCNen-ja	2019/07/26 08:51:18	2961	0.853287	0.852723	0.853165	-	-	-	-	-	-	-	NMT	No	Base Transformer
4	KNU_Hyundai	JPCNen-ja	2019/07/27 11:59:37	3184	0.863639	0.862583	0.863678	-	-	-	-	-	-	-	NMT	No	Transformer Base, relative position, BT, r2l reranking, checkpoint ensemble
5	KNU_Hyundai	JPCNen-ja	2019/07/27 12:48:32	3192	0.866402	0.865316	0.866282	-	-	-	-	-	-	-	NMT	Yes	Transformer Base (+ASPEC data), relative position, BT, r2l reranking, checkpoint ensemble
6	goku20	JPCNen-ja	2020/09/18 17:20:54	3922	0.855646	0.855196	0.855529	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 3 models
7	goku20	JPCNen-ja	2020/09/18 17:31:17	3929	0.855454	0.854173	0.855317	-	-	-	-	-	-	-	NMT	No	mBART pre-training, ensemble of 3 models
8	TMU	JPCNen-ja	2021/04/22 07:34:06	5347	0.854558	0.853335	0.854298	-	-	-	-	-	-	-	NMT	No	Japanese BART, ensemble of 3 models
9	tpt_wat	JPCNen-ja	2021/04/27 02:23:00	5705	0.863837	0.862481	0.863497	-	-	-	-	-	-	-	NMT	No	Base Transformer model with joint vocab, size 8k
10	Bering Lab	JPCNen-ja	2021/05/04 20:56:31	6391	0.866967	0.865739	0.866656	-	-	-	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
11	ORGANIZER	JPCNen-ja	2018/08/16 16:40:36	1999	0.840117	0.838359	0.839361	-	-	-	-	-	0.000000	0.000000	NMT	No	NMT with Attention
12	EHR	JPCNen-ja	2018/09/08 20:24:32	2247	0.855043	0.852770	0.854429	-	-	-	-	-	0.000000	0.000000	NMT	No	SMT reranked NMT
13	EHR	JPCNen-ja	2018/09/13 12:49:07	2283	0.852484	0.850664	0.852467	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 13)
14	EHR	JPCNen-ja	2018/09/15 15:47:49	2394	0.854902	0.852746	0.854868	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 18)
15	EHR	JPCNen-ja	2018/09/16 15:07:27	2475	0.854848	0.853007	0.854933	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 20)

Notice:

This table is sorted by the leftmost segmenters. You can change the segmenter used to sort by clicking each segmenter link.

AMFM

#	Team	Task	Date/Time	DataID	AMFM										Method	Other Resources	System Description
#	Team	Task	Date/Time	DataID	unuse	unuse	unuse	unuse	unuse	unuse	unuse	unuse	unuse	unuse	Method	Other Resources	System Description
1	sarah	JPCNen-ja	2019/07/22 14:59:33	2804	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Transformer, single model
2	sarah	JPCNen-ja	2019/07/25 13:02:06	2926	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
3	ryan	JPCNen-ja	2019/07/26 08:51:18	2961	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Base Transformer
4	KNU_Hyundai	JPCNen-ja	2019/07/27 11:59:37	3184	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Transformer Base, relative position, BT, r2l reranking, checkpoint ensemble
5	KNU_Hyundai	JPCNen-ja	2019/07/27 12:48:32	3192	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	Yes	Transformer Base (+ASPEC data), relative position, BT, r2l reranking, checkpoint ensemble
6	goku20	JPCNen-ja	2020/09/18 17:20:54	3922	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 3 models
7	goku20	JPCNen-ja	2020/09/18 17:31:17	3929	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	mBART pre-training, ensemble of 3 models
8	TMU	JPCNen-ja	2021/04/22 07:34:06	5347	0.876323	0.876323	0.876323	-	-	-	-	-	-	-	NMT	No	Japanese BART, ensemble of 3 models
9	tpt_wat	JPCNen-ja	2021/04/27 02:23:00	5705	0.878451	0.878451	0.878451	-	-	-	-	-	-	-	NMT	No	Base Transformer model with joint vocab, size 8k
10	Bering Lab	JPCNen-ja	2021/05/04 20:56:31	6391	0.880142	0.880142	0.880142	-	-	-	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
11	ORGANIZER	JPCNen-ja	2018/08/16 16:40:36	1999	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	No	NMT with Attention
12	EHR	JPCNen-ja	2018/09/08 20:24:32	2247	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	No	SMT reranked NMT
13	EHR	JPCNen-ja	2018/09/13 12:49:07	2283	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 13)
14	EHR	JPCNen-ja	2018/09/15 15:47:49	2394	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 18)
15	EHR	JPCNen-ja	2018/09/16 15:07:27	2475	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 20)

Notice:

This table is sorted by the leftmost segmenters. You can change the segmenter used to sort by clicking each segmenter link.
Adequacy-Fluency Metrics (AMFM) is a two-dimensional automatic evaluation metric for machine translation, designed to operate at the sentence level. It is based on adequacy and fluency, to decouple semantic and syntactic components of the translation process to provide a balanced view on translation quality.
AMFM is calculated without tokenizers.
The detail of AMFM is shown on the following paper: "Adequacy–Fluency Metrics: Evaluating MT in the Continuous Space Model Framework" [pdf]. Invited Talk in WAT2015 also helps understanding [slide].

HUMAN (WAT2022)

Notice:

HUMAN (WAT2022) is the result of the Pairwise Crowdsourcing Evaluation on WAT2022.
HUMAN (WAT2022) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2021)

Notice:

HUMAN (WAT2021) is the result of the Pairwise Crowdsourcing Evaluation on WAT2021.
HUMAN (WAT2021) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2020)

Notice:

HUMAN (WAT2020) is the result of the Pairwise Crowdsourcing Evaluation on WAT2020.
HUMAN (WAT2020) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2019)

Notice:

HUMAN (WAT2019) is the result of the Pairwise Crowdsourcing Evaluation on WAT2019.
HUMAN (WAT2019) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2018)

Notice:

HUMAN (WAT2018) is the result of the Pairwise Crowdsourcing Evaluation on WAT2018.
HUMAN (WAT2018) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2017)

Notice:

HUMAN (WAT2017) is the result of the Pairwise Crowdsourcing Evaluation on WAT2017.
HUMAN (WAT2017) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2016)

Notice:

HUMAN (WAT2016) is the result of the Pairwise Crowdsourcing Evaluation on WAT2016.
HUMAN (WAT2016) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.

HUMAN (WAT2015)

Notice:

HUMAN (WAT2015) is the result of the Pairwise Crowdsourcing Evaluation on WAT2015.
HUMAN (WAT2015) was evaluated by 5 different workers and the final decision is made by the voting of the judgements.
The detail of the evaluation can be found in the pdf document (PDF file).

HUMAN (WAT2014)

Notice:

HUMAN (WAT2014) is the result of the Pairwise Crowdsourcing Evaluation on WAT2014.
HUMAN (WAT2014) was evaluated by 3 different workers and the final decision is made by the voting of the judgements.
The detail of the evaluation can be found in the pdf document (PDF file).

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

WAT The Workshop on Asian Translation Evaluation Results

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

WAT

The Workshop on Asian Translation

Evaluation Results