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	ryan	JPCN2ja-zh	2019/07/25 22:06:17	2951	-	41.68	-	-	42.63	42.26	-	-	-	-	NMT	No	Base Transformer
2	sarah	JPCN2ja-zh	2019/07/26 11:40:15	2983	-	42.23	-	-	43.19	42.98	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
3	KNU_Hyundai	JPCN2ja-zh	2019/07/27 08:39:21	3165	-	44.09	-	-	45.23	44.68	-	-	-	-	NMT	Yes	Transformer(base) + Used ASPEC corpus with relative position, bt, r2l rerank, 4-model ensemble(9-check point)
4	goku20	JPCN2ja-zh	2020/09/21 12:09:42	4090	-	40.84	-	-	41.75	41.43	-	-	-	-	NMT	No	mBART pre-training transformer, single model
5	goku20	JPCN2ja-zh	2020/09/22 00:10:38	4110	-	41.93	-	-	42.84	42.52	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
6	goku20	JPCN2ja-zh	2020/09/22 00:12:52	4111	-	41.93	-	-	42.84	42.52	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
7	tpt_wat	JPCN2ja-zh	2021/04/27 01:50:28	5695	-	38.76	-	-	39.71	39.47	-	-	-	-	SMT	No	Base Transformer Model with separate vocabularies, 8k size
8	tpt_wat	JPCN2ja-zh	2021/04/27 01:51:04	5696	-	38.76	-	-	39.71	39.47	-	-	-	-	SMT	No	Base Transformer Model with separate vocabularies, 8k size
9	Bering Lab	JPCN2ja-zh	2021/04/30 12:39:49	5848	-	43.98	-	-	45.01	44.72	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
10	sakura	JPCN2ja-zh	2024/08/09 00:42:32	7304	-	40.60	-	-	41.40	41.10	-	-	-	-	NMT	No	LLM: Rakuten/RakutenAI-7B-chat Fine-Tuned with JPC Corpus in six direction (En-Ja, Ja-En, Ko-Ja, Ja-Ko, Zh-Ja, Ja-Zh) - Best
11	sakura	JPCN2ja-zh	2024/08/09 00:44:49	7305	-	40.30	-	-	41.20	40.80	-	-	-	-	NMT	No	LLM: Rakuten/RakutenAI-7B-chat Fine-Tuned with JPC Corpus in six direction (En-Ja, Ja-En, Ko-Ja, Ja-Ko, Zh-Ja, Ja-Zh)
12	ORGANIZER	JPCN2ja-zh	2018/08/15 18:23:17	1961	-	39.14	-	-	40.28	39.77	-	-	0.00	0.00	NMT	No	NMT with Attention
13	USTC	JPCN2ja-zh	2018/08/31 17:00:53	2203	-	39.91	-	-	40.53	40.32	-	-	0.00	0.00	NMT	No	tensor2tensor, 4 model average, r2l rerank

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	ryan	JPCN2ja-zh	2019/07/25 22:06:17	2951	-	0.857546	-	-	0.862599	0.861150	-	-	-	-	NMT	No	Base Transformer
2	sarah	JPCN2ja-zh	2019/07/26 11:40:15	2983	-	0.854775	-	-	0.860837	0.859031	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
3	KNU_Hyundai	JPCN2ja-zh	2019/07/27 08:39:21	3165	-	0.869556	-	-	0.874140	0.872997	-	-	-	-	NMT	Yes	Transformer(base) + Used ASPEC corpus with relative position, bt, r2l rerank, 4-model ensemble(9-check point)
4	goku20	JPCN2ja-zh	2020/09/21 12:09:42	4090	-	0.853709	-	-	0.859872	0.858557	-	-	-	-	NMT	No	mBART pre-training transformer, single model
5	goku20	JPCN2ja-zh	2020/09/22 00:10:38	4110	-	0.857293	-	-	0.863419	0.861801	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
6	goku20	JPCN2ja-zh	2020/09/22 00:12:52	4111	-	0.857293	-	-	0.863419	0.861801	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
7	tpt_wat	JPCN2ja-zh	2021/04/27 01:50:28	5695	-	0.844570	-	-	0.849819	0.848185	-	-	-	-	SMT	No	Base Transformer Model with separate vocabularies, 8k size
8	tpt_wat	JPCN2ja-zh	2021/04/27 01:51:04	5696	-	0.844570	-	-	0.849819	0.848185	-	-	-	-	SMT	No	Base Transformer Model with separate vocabularies, 8k size
9	Bering Lab	JPCN2ja-zh	2021/04/30 12:39:49	5848	-	0.868941	-	-	0.873382	0.872298	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
10	sakura	JPCN2ja-zh	2024/08/09 00:42:32	7304	-	0.854800	-	-	0.860833	0.859124	-	-	-	-	NMT	No	LLM: Rakuten/RakutenAI-7B-chat Fine-Tuned with JPC Corpus in six direction (En-Ja, Ja-En, Ko-Ja, Ja-Ko, Zh-Ja, Ja-Zh) - Best
11	sakura	JPCN2ja-zh	2024/08/09 00:44:49	7305	-	0.854198	-	-	0.860137	0.858608	-	-	-	-	NMT	No	LLM: Rakuten/RakutenAI-7B-chat Fine-Tuned with JPC Corpus in six direction (En-Ja, Ja-En, Ko-Ja, Ja-Ko, Zh-Ja, Ja-Zh)
12	ORGANIZER	JPCN2ja-zh	2018/08/15 18:23:17	1961	-	0.847486	-	-	0.852161	0.850707	-	-	0.000000	0.000000	NMT	No	NMT with Attention
13	USTC	JPCN2ja-zh	2018/08/31 17:00:53	2203	-	0.853978	-	-	0.859330	0.857456	-	-	0.000000	0.000000	NMT	No	tensor2tensor, 4 model average, r2l rerank

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	ryan	JPCN2ja-zh	2019/07/25 22:06:17	2951	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	Base Transformer
2	sarah	JPCN2ja-zh	2019/07/26 11:40:15	2983	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	Transformer, ensemble of 4 models
3	KNU_Hyundai	JPCN2ja-zh	2019/07/27 08:39:21	3165	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	Yes	Transformer(base) + Used ASPEC corpus with relative position, bt, r2l rerank, 4-model ensemble(9-check point)
4	goku20	JPCN2ja-zh	2020/09/21 12:09:42	4090	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	mBART pre-training transformer, single model
5	goku20	JPCN2ja-zh	2020/09/22 00:10:38	4110	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
6	goku20	JPCN2ja-zh	2020/09/22 00:12:52	4111	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	mBART pre-training transformer, ensemble of 3 models
7	tpt_wat	JPCN2ja-zh	2021/04/27 01:50:28	5695	-	0.888029	-	-	0.888029	0.888029	-	-	-	-	SMT	No	Base Transformer Model with separate vocabularies, 8k size
8	tpt_wat	JPCN2ja-zh	2021/04/27 01:51:04	5696	-	0.888029	-	-	0.888029	0.888029	-	-	-	-	SMT	No	Base Transformer Model with separate vocabularies, 8k size
9	Bering Lab	JPCN2ja-zh	2021/04/30 12:39:49	5848	-	0.893837	-	-	0.893837	0.893837	-	-	-	-	NMT	Yes	Transformer Ensemble with additional crawled parallel corpus
10	sakura	JPCN2ja-zh	2024/08/09 00:42:32	7304	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	LLM: Rakuten/RakutenAI-7B-chat Fine-Tuned with JPC Corpus in six direction (En-Ja, Ja-En, Ko-Ja, Ja-Ko, Zh-Ja, Ja-Zh) - Best
11	sakura	JPCN2ja-zh	2024/08/09 00:44:49	7305	-	0.000000	-	-	0.000000	0.000000	-	-	-	-	NMT	No	LLM: Rakuten/RakutenAI-7B-chat Fine-Tuned with JPC Corpus in six direction (En-Ja, Ja-En, Ko-Ja, Ja-Ko, Zh-Ja, Ja-Zh)
12	ORGANIZER	JPCN2ja-zh	2018/08/15 18:23:17	1961	-	0.000000	-	-	0.000000	0.000000	-	-	0.000000	0.000000	NMT	No	NMT with Attention
13	USTC	JPCN2ja-zh	2018/08/31 17:00:53	2203	-	0.000000	-	-	0.000000	0.000000	-	-	0.000000	0.000000	NMT	No	tensor2tensor, 4 model average, r2l rerank

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