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	Bering Lab	JPCN2en-ja	2021/05/04 19:43:01	6350	44.51	46.43	44.63	-	-	-	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
2	KNU_Hyundai	JPCN2en-ja	2019/07/27 12:52:40	3194	44.30	46.22	44.45	-	-	-	-	-	-	-	NMT	Yes	Transformer Base (+ASPEC data), relative position, BT, r2l reranking, checkpoint ensemble
3	tpt_wat	JPCN2en-ja	2021/04/27 02:28:01	5707	44.23	46.18	44.45	-	-	-	-	-	-	-	NMT	No	Base Transformer model with joint vocab, size 8k
4	KNU_Hyundai	JPCN2en-ja	2019/07/27 12:07:31	3186	44.16	46.28	44.41	-	-	-	-	-	-	-	NMT	No	Transformer Base, relative position, BT, r2l reranking, checkpoint ensemble
5	sarah	JPCN2en-ja	2019/07/26 11:26:34	2974	42.02	44.56	42.35	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
6	EHR	JPCN2en-ja	2018/09/16 15:10:44	2477	42.12	43.76	42.06	-	-	-	-	-	0.00	0.00	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 20)
7	goku20	JPCN2en-ja	2020/09/22 00:14:35	4112	41.60	44.14	42.01	-	-	-	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
8	EHR	JPCN2en-ja	2018/09/15 15:52:37	2396	41.99	43.63	41.90	-	-	-	-	-	0.00	0.00	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 18)
9	goku20	JPCN2en-ja	2020/09/21 12:19:14	4097	41.39	43.84	41.73	-	-	-	-	-	-	-	NMT	No	mBART pre-training transformer, single model
10	EHR	JPCN2en-ja	2018/09/13 12:52:17	2285	41.54	43.17	41.53	-	-	-	-	-	0.00	0.00	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 13)
11	EHR	JPCN2en-ja	2018/09/08 20:34:49	2249	41.45	43.18	41.39	-	-	-	-	-	0.00	0.00	NMT	No	SMT reranked NMT
12	ORGANIZER	JPCN2en-ja	2018/08/15 12:47:37	1936	38.51	40.60	38.47	-	-	-	-	-	0.00	0.00	NMT	No	NMT with Attention

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	Bering Lab	JPCN2en-ja	2021/05/04 19:43:01	6350	0.855721	0.855391	0.855994	-	-	-	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
2	KNU_Hyundai	JPCN2en-ja	2019/07/27 12:52:40	3194	0.852425	0.852467	0.852636	-	-	-	-	-	-	-	NMT	Yes	Transformer Base (+ASPEC data), relative position, BT, r2l reranking, checkpoint ensemble
3	tpt_wat	JPCN2en-ja	2021/04/27 02:28:01	5707	0.851325	0.850992	0.851503	-	-	-	-	-	-	-	NMT	No	Base Transformer model with joint vocab, size 8k
4	KNU_Hyundai	JPCN2en-ja	2019/07/27 12:07:31	3186	0.850334	0.849849	0.850692	-	-	-	-	-	-	-	NMT	No	Transformer Base, relative position, BT, r2l reranking, checkpoint ensemble
5	goku20	JPCN2en-ja	2020/09/22 00:14:35	4112	0.842811	0.842266	0.842952	-	-	-	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
6	EHR	JPCN2en-ja	2018/09/16 15:10:44	2477	0.840713	0.839052	0.841133	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 20)
7	EHR	JPCN2en-ja	2018/09/15 15:52:37	2396	0.840474	0.838704	0.840707	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 18)
8	goku20	JPCN2en-ja	2020/09/21 12:19:14	4097	0.840442	0.839955	0.840468	-	-	-	-	-	-	-	NMT	No	mBART pre-training transformer, single model
9	EHR	JPCN2en-ja	2018/09/08 20:34:49	2249	0.840811	0.838725	0.840057	-	-	-	-	-	0.000000	0.000000	NMT	No	SMT reranked NMT
10	sarah	JPCN2en-ja	2019/07/26 11:26:34	2974	0.837381	0.837594	0.837902	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
11	EHR	JPCN2en-ja	2018/09/13 12:52:17	2285	0.837564	0.836473	0.837793	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 13)
12	ORGANIZER	JPCN2en-ja	2018/08/15 12:47:37	1936	0.825565	0.824420	0.824770	-	-	-	-	-	0.000000	0.000000	NMT	No	NMT with Attention

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	Bering Lab	JPCN2en-ja	2021/05/04 19:43:01	6350	0.872870	0.872870	0.872870	-	-	-	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
2	tpt_wat	JPCN2en-ja	2021/04/27 02:28:01	5707	0.870086	0.870086	0.870086	-	-	-	-	-	-	-	NMT	No	Base Transformer model with joint vocab, size 8k
3	ORGANIZER	JPCN2en-ja	2018/08/15 12:47:37	1936	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	No	NMT with Attention
4	EHR	JPCN2en-ja	2018/09/08 20:34:49	2249	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	No	SMT reranked NMT
5	EHR	JPCN2en-ja	2018/09/13 12:52:17	2285	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 13)
6	EHR	JPCN2en-ja	2018/09/15 15:52:37	2396	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 18)
7	EHR	JPCN2en-ja	2018/09/16 15:10:44	2477	0.000000	0.000000	0.000000	-	-	-	-	-	0.000000	0.000000	NMT	Yes	SMT reranked NMT (4M traning data from WAT and NTCIR, Epoch 20)
8	sarah	JPCN2en-ja	2019/07/26 11:26:34	2974	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
9	KNU_Hyundai	JPCN2en-ja	2019/07/27 12:07:31	3186	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	Transformer Base, relative position, BT, r2l reranking, checkpoint ensemble
10	KNU_Hyundai	JPCN2en-ja	2019/07/27 12:52:40	3194	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	Yes	Transformer Base (+ASPEC data), relative position, BT, r2l reranking, checkpoint ensemble
11	goku20	JPCN2en-ja	2020/09/21 12:19:14	4097	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	mBART pre-training transformer, single model
12	goku20	JPCN2en-ja	2020/09/22 00:14:35	4112	0.000000	0.000000	0.000000	-	-	-	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models

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