Along with the data files, we also provide:
- subword-nmt as a GIT submodule
- A recent (December 2017) snapshot of Moses preprocessing scripts
under scripts/ in order to minimize processing differences across the users.
In order to fetch everything correctly, you need to clone the repository with --recursive flag:
$ git clone --recursive https://github.com/multi30k/dataset.git multi30k-dataset
Pre-extracted visual features can be downloaded from Google Drive and the raw images can be requested here.
- Raw files under data/task1/raw
- Tokenized files under data/task1/tok. These files were produced with the preprocessing script scripts/task1-tokenize.sh.
You can evaluate your model on the 2018 test sets using the ongoing Codalab competition.
train
(en) 29000 sentences, 377534 words, 13.0 words/sent
(de) 29000 sentences, 360706 words, 12.4 words/sent
(fr) 29000 sentences, 409845 words, 14.1 words/sent
(cs) 29000 sentences, 297212 words, 10.2 words/sent
val
(en) 1014 sentences, 13308 words, 13.1 words/sent
(de) 1014 sentences, 12828 words, 12.7 words/sent
(fr) 1014 sentences, 14381 words, 14.2 words/sent
(cs) 1014 sentences, 10342 words, 10.2 words/sent
test_2016_flickr
(en) 1000 sentences, 12968 words, 13.0 words/sent
(de) 1000 sentences, 12103 words, 12.1 words/sent
(fr) 1000 sentences, 13988 words, 14.0 words/sent
(cs) 1000 sentences, 10497 words, 10.5 words/sent
test_2017_flickr
(en) 1000 sentences, 11376 words, 11.4 words/sent
(de) 1000 sentences, 10758 words, 10.8 words/sent
(fr) 1000 sentences, 12596 words, 12.6 words/sent
test_2017_mscoco
(en) 461 sentences, 5239 words, 11.4 words/sent
(de) 461 sentences, 5158 words, 11.2 words/sent
(fr) 461 sentences, 5710 words, 12.4 words/sent
If you use these resources in your research, please consider citing the following papers:
English and German data:
@InProceedings{W16-3210,
author = "Elliott, Desmond
and Frank, Stella
and Sima'an, Khalil
and Specia, Lucia",
title = "Multi30K: Multilingual English-German Image Descriptions",
booktitle = "Proceedings of the 5th Workshop on Vision and Language",
year = "2016",
publisher = "Association for Computational Linguistics",
pages = "70--74",
location = "Berlin, Germany",
doi = "10.18653/v1/W16-3210",
url = "http://www.aclweb.org/anthology/W16-3210"
}
French data, Ambiguous COCO evaluation data, and Test 2017 data:
@InProceedings{elliott-EtAl:2017:WMT,
author = {Elliott, Desmond and Frank, Stella and Barrault, Lo\"{i}c and Bougares, Fethi and Specia, Lucia},
title = {Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description},
booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers},
month = {September},
year = {2017},
address = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
pages = {215--233},
url = {http://www.aclweb.org/anthology/W17-4718}
}
Czech data:
@inproceedings{barrault2018findings,
title={Findings of the Third Shared Task on Multimodal Machine Translation},
author={Barrault, Lo{\"\i}c and Bougares, Fethi and Specia, Lucia and Lala, Chiraag and Elliott, Desmond and Frank, Stella},
booktitle={Proceedings of the Third Conference on Machine Translation: Shared Task Papers},
pages={304--323},
year={2018}
}
Thanks to Oliver Maunoury and Laure Behue for producing most of French Translations of 2018 Test set.
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