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	BITS-P	MMEVMM23en-hi	2023/07/08 13:46:53	7125	-	-	-	-	-	-	45.00	-	-	-	NMT	Yes	NLLB model finetuned on captions + object tags of original & synthetic images using DETR model
2	Volta	MMEVMM23en-hi	2021/05/25 13:55:52	6428	-	-	-	-	-	-	44.64	-	-	-	NMT	Yes	Finetuned mBART (Used IITB for data augmentation) and added object tags to the input using Mask RCNN
3	iitp	MMEVMM23en-hi	2021/05/01 23:19:14	5941	-	-	-	-	-	-	42.47	-	-	-	NMT	No	Removed special chars at start and end of sentnce 1. pre-trained with HindEnCorp, trained with Vis Gen 2. trained with Visual Genome Selected best of two for each sentence according to translation
4	SILO_NLP	MMEVMM23en-hi	2022/07/18 17:31:41	6958	-	-	-	-	-	-	42.00	-	-	-	NMT	Yes	Object Tags (Image) + Flickr8 dataset as additional resource + Finetune mBART
5	ODIAGEN	MMEVMM23en-hi	2023/07/06 03:45:52	7105	-	-	-	-	-	-	41.60	-	-	-	NMT	No	Image features extracted as Object tags appended with text and MBART fine-tuning
6	CNLP-NITS	MMEVMM23en-hi	2020/09/18 15:59:02	3896	-	-	-	-	-	-	40.51	-	-	-	NMT	Yes	Pretrained monolingual data (IITB) using Glove and fine-tuned with parallel data and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
7	CNLP-NITS-PP	MMEVMM23en-hi	2021/04/28 00:05:19	5731	-	-	-	-	-	-	39.46	-	-	-	NMT	Yes	Pretrained monolingual data (IITB) using Glove and fine-tuned with parallel data (WAT21 train data+ Extracted Phrase pairs from WAT21 train data +IITB train data) and visual features in training using
8	CNLP-NITS-PP	MMEVMM23en-hi	2022/07/11 12:31:32	6740	-	-	-	-	-	-	39.40	-	-	-	NMT	No	Transliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
9	ORGANIZER	MMEVMM23en-hi	2020/11/07 01:54:46	4183	-	-	-	-	-	-	38.63	-	-	-	NMT	No
10	ORGANIZER	MMEVMM23en-hi	2020/11/07 01:49:40	4182	-	-	-	-	-	-	27.41	-	-	-	NMT	No

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	BITS-P	MMEVMM23en-hi	2023/07/08 13:46:53	7125	-	-	-	-	-	-	0.829320	-	-	-	NMT	Yes	NLLB model finetuned on captions + object tags of original & synthetic images using DETR model
2	Volta	MMEVMM23en-hi	2021/05/25 13:55:52	6428	-	-	-	-	-	-	0.823319	-	-	-	NMT	Yes	Finetuned mBART (Used IITB for data augmentation) and added object tags to the input using Mask RCNN
3	ODIAGEN	MMEVMM23en-hi	2023/07/06 03:45:52	7105	-	-	-	-	-	-	0.811420	-	-	-	NMT	No	Image features extracted as Object tags appended with text and MBART fine-tuning
4	iitp	MMEVMM23en-hi	2021/05/01 23:19:14	5941	-	-	-	-	-	-	0.807123	-	-	-	NMT	No	Removed special chars at start and end of sentnce 1. pre-trained with HindEnCorp, trained with Vis Gen 2. trained with Visual Genome Selected best of two for each sentence according to translation
5	CNLP-NITS	MMEVMM23en-hi	2020/09/18 15:59:02	3896	-	-	-	-	-	-	0.803208	-	-	-	NMT	Yes	Pretrained monolingual data (IITB) using Glove and fine-tuned with parallel data and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
6	CNLP-NITS-PP	MMEVMM23en-hi	2022/07/11 12:31:32	6740	-	-	-	-	-	-	0.802635	-	-	-	NMT	No	Transliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
7	CNLP-NITS-PP	MMEVMM23en-hi	2021/04/28 00:05:19	5731	-	-	-	-	-	-	0.802055	-	-	-	NMT	Yes	Pretrained monolingual data (IITB) using Glove and fine-tuned with parallel data (WAT21 train data+ Extracted Phrase pairs from WAT21 train data +IITB train data) and visual features in training using
8	SILO_NLP	MMEVMM23en-hi	2022/07/18 17:31:41	6958	-	-	-	-	-	-	0.796441	-	-	-	NMT	Yes	Object Tags (Image) + Flickr8 dataset as additional resource + Finetune mBART
9	ORGANIZER	MMEVMM23en-hi	2020/11/07 01:54:46	4183	-	-	-	-	-	-	0.767422	-	-	-	NMT	No
10	ORGANIZER	MMEVMM23en-hi	2020/11/07 01:49:40	4182	-	-	-	-	-	-	0.651455	-	-	-	NMT	No

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	Volta	MMEVMM23en-hi	2021/05/25 13:55:52	6428	-	-	-	-	-	-	0.839100	-	-	-	NMT	Yes	Finetuned mBART (Used IITB for data augmentation) and added object tags to the input using Mask RCNN
2	CNLP-NITS	MMEVMM23en-hi	2020/09/18 15:59:02	3896	-	-	-	-	-	-	0.820980	-	-	-	NMT	Yes	Pretrained monolingual data (IITB) using Glove and fine-tuned with parallel data and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
3	ORGANIZER	MMEVMM23en-hi	2020/11/07 01:54:46	4183	-	-	-	-	-	-	0.772870	-	-	-	NMT	No
4	ORGANIZER	MMEVMM23en-hi	2020/11/07 01:49:40	4182	-	-	-	-	-	-	0.691950	-	-	-	NMT	No
5	CNLP-NITS-PP	MMEVMM23en-hi	2021/04/28 00:05:19	5731	-	-	-	-	-	-	0.641430	-	-	-	NMT	Yes	Pretrained monolingual data (IITB) using Glove and fine-tuned with parallel data (WAT21 train data+ Extracted Phrase pairs from WAT21 train data +IITB train data) and visual features in training using
6	iitp	MMEVMM23en-hi	2021/05/01 23:19:14	5941	-	-	-	-	-	-	0.629444	-	-	-	NMT	No	Removed special chars at start and end of sentnce 1. pre-trained with HindEnCorp, trained with Vis Gen 2. trained with Visual Genome Selected best of two for each sentence according to translation
7	CNLP-NITS-PP	MMEVMM23en-hi	2022/07/11 12:31:32	6740	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	Transliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
8	SILO_NLP	MMEVMM23en-hi	2022/07/18 17:31:41	6958	-	-	-	-	-	-	0.000000	-	-	-	NMT	Yes	Object Tags (Image) + Flickr8 dataset as additional resource + Finetune mBART
9	ODIAGEN	MMEVMM23en-hi	2023/07/06 03:45:52	7105	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	Image features extracted as Object tags appended with text and MBART fine-tuning
10	BITS-P	MMEVMM23en-hi	2023/07/08 13:46:53	7125	-	-	-	-	-	-	0.000000	-	-	-	NMT	Yes	NLLB model finetuned on captions + object tags of original & synthetic images using DETR model

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