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	CNLP-NITS-PP	MMEVMM24en-bn	2022/07/11 12:51:09	6743	-	-	-	-	-	-	43.90	-	-	-	NMT	No	Transliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
2	SILO_NLP	MMEVMM24en-bn	2022/07/14 20:56:54	6939	-	-	-	-	-	-	42.10	-	-	-	NMT	No	Object Tags (Image) + Finetune mBART
3	ODIAGEN	MMEVMM24en-bn	2023/07/06 04:02:32	7107	-	-	-	-	-	-	42.40	-	-	-	NMT	No	Image features extracted as Object tags appended with text and MBART fine-tuning
4	BITS-P	MMEVMM24en-bn	2023/07/08 13:40:55	7123	-	-	-	-	-	-	50.60	-	-	-	NMT	Yes	NLLB model finetuned on captions + object tags of original & synthetic images using DETR model
5	1128	MMEVMM24en-bn	2024/07/23 15:43:17	7137	-	-	-	-	-	-	40.60	-	-	-	NMT	No	initial model
6	00-7	MMEVMM24en-bn	2024/08/05 15:15:26	7191	-	-	-	-	-	-	46.40	-	-	-	NMT	Yes	TEST
7	v036	MMEVMM24en-bn	2024/08/11 12:34:14	7317	-	-	-	-	-	-	18.00	-	-	-	NMT	No	NMT based system using both image descriptors and text description. A multistage LLM pipeline used for extracting image data descriptions and translation. Fine tuning done in few cases Models Used:
8	239233	MMEVMM24en-bn	2024/08/13 12:28:24	7381	-	-	-	-	-	-	22.10	-	-	-	NMT	Yes	One-shot prompt for synthetic QA description from captions; translate QA using IndicTrans2; generate caption from QA as context
9	UNLP	MMEVMM24en-bn	2024/08/13 17:55:18	7391	-	-	-	-	-	-	42.10	-	-	-	NMT	No	Using the Transformer-based Gated Fusion model to integrate both text and visual data.
10	v036	MMEVMM24en-bn	2024/08/15 19:53:41	7417	-	-	-	-	-	-	41.40	-	-	-	NMT	No
11	v036	MMEVMM24en-bn	2024/08/15 19:55:30	7418	-	-	-	-	-	-	44.10	-	-	-	NMT	No
12	IITP-AI-NLP-ML	MMEVMM24en-bn	2025/10/22 21:33:19	7457	-	-	-	-	-	-	48.70	-	-	-	SMT	Yes	Used Selective Attention Architecture with IndicTrans as the base model and CLIP ViT-B/16 model to extract image features. We extract a) Full image feats, and b) cropped image feats and pick the one w

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	CNLP-NITS-PP	MMEVMM24en-bn	2022/07/11 12:51:09	6743	-	-	-	-	-	-	0.780669	-	-	-	NMT	No	Transliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
2	SILO_NLP	MMEVMM24en-bn	2022/07/14 20:56:54	6939	-	-	-	-	-	-	0.754291	-	-	-	NMT	No	Object Tags (Image) + Finetune mBART
3	ODIAGEN	MMEVMM24en-bn	2023/07/06 04:02:32	7107	-	-	-	-	-	-	0.763497	-	-	-	NMT	No	Image features extracted as Object tags appended with text and MBART fine-tuning
4	BITS-P	MMEVMM24en-bn	2023/07/08 13:40:55	7123	-	-	-	-	-	-	0.814207	-	-	-	NMT	Yes	NLLB model finetuned on captions + object tags of original & synthetic images using DETR model
5	1128	MMEVMM24en-bn	2024/07/23 15:43:17	7137	-	-	-	-	-	-	0.755151	-	-	-	NMT	No	initial model
6	00-7	MMEVMM24en-bn	2024/08/05 15:15:26	7191	-	-	-	-	-	-	0.775597	-	-	-	NMT	Yes	TEST
7	v036	MMEVMM24en-bn	2024/08/11 12:34:14	7317	-	-	-	-	-	-	0.528613	-	-	-	NMT	No	NMT based system using both image descriptors and text description. A multistage LLM pipeline used for extracting image data descriptions and translation. Fine tuning done in few cases Models Used:
8	239233	MMEVMM24en-bn	2024/08/13 12:28:24	7381	-	-	-	-	-	-	0.575370	-	-	-	NMT	Yes	One-shot prompt for synthetic QA description from captions; translate QA using IndicTrans2; generate caption from QA as context
9	UNLP	MMEVMM24en-bn	2024/08/13 17:55:18	7391	-	-	-	-	-	-	0.766589	-	-	-	NMT	No	Using the Transformer-based Gated Fusion model to integrate both text and visual data.
10	v036	MMEVMM24en-bn	2024/08/15 19:53:41	7417	-	-	-	-	-	-	0.720732	-	-	-	NMT	No
11	v036	MMEVMM24en-bn	2024/08/15 19:55:30	7418	-	-	-	-	-	-	0.737924	-	-	-	NMT	No
12	IITP-AI-NLP-ML	MMEVMM24en-bn	2025/10/22 21:33:19	7457	-	-	-	-	-	-	0.799718	-	-	-	SMT	Yes	Used Selective Attention Architecture with IndicTrans as the base model and CLIP ViT-B/16 model to extract image features. We extract a) Full image feats, and b) cropped image feats and pick the one w

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	CNLP-NITS-PP	MMEVMM24en-bn	2022/07/11 12:51:09	6743	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	Transliteration-based phrase pairs augmentation and visual features in training using BRNN encoder and doubly-attentive-rnn decoder.
2	SILO_NLP	MMEVMM24en-bn	2022/07/14 20:56:54	6939	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	Object Tags (Image) + Finetune mBART
3	ODIAGEN	MMEVMM24en-bn	2023/07/06 04:02:32	7107	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	Image features extracted as Object tags appended with text and MBART fine-tuning
4	BITS-P	MMEVMM24en-bn	2023/07/08 13:40:55	7123	-	-	-	-	-	-	0.000000	-	-	-	NMT	Yes	NLLB model finetuned on captions + object tags of original & synthetic images using DETR model
5	1128	MMEVMM24en-bn	2024/07/23 15:43:17	7137	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	initial model
6	00-7	MMEVMM24en-bn	2024/08/05 15:15:26	7191	-	-	-	-	-	-	0.000000	-	-	-	NMT	Yes	TEST
7	v036	MMEVMM24en-bn	2024/08/11 12:34:14	7317	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	NMT based system using both image descriptors and text description. A multistage LLM pipeline used for extracting image data descriptions and translation. Fine tuning done in few cases Models Used:
8	239233	MMEVMM24en-bn	2024/08/13 12:28:24	7381	-	-	-	-	-	-	0.000000	-	-	-	NMT	Yes	One-shot prompt for synthetic QA description from captions; translate QA using IndicTrans2; generate caption from QA as context
9	UNLP	MMEVMM24en-bn	2024/08/13 17:55:18	7391	-	-	-	-	-	-	0.000000	-	-	-	NMT	No	Using the Transformer-based Gated Fusion model to integrate both text and visual data.
10	v036	MMEVMM24en-bn	2024/08/15 19:53:41	7417	-	-	-	-	-	-	0.000000	-	-	-	NMT	No
11	v036	MMEVMM24en-bn	2024/08/15 19:55:30	7418	-	-	-	-	-	-	0.000000	-	-	-	NMT	No
12	IITP-AI-NLP-ML	MMEVMM24en-bn	2025/10/22 21:33:19	7457	-	-	-	-	-	-	0.000000	-	-	-	SMT	Yes	Used Selective Attention Architecture with IndicTrans as the base model and CLIP ViT-B/16 model to extract image features. We extract a) Full image feats, and b) cropped image feats and pick the one w

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