Data:
Cross-lingual Visual Pre-training for Multimodal Machine Translation

dc.contributor.authorOzan Caglayan
dc.contributor.authorMenekse Kuyu
dc.contributor.authorMustafa Sercan Amac
dc.contributor.authorPranava Madhyastha
dc.contributor.authorErkut Erdem
dc.contributor.authorAykut Erdem
dc.contributor.authorLucia Specia
dc.contributor.orcid0000-0002-5992-3470
dc.date.accessioned2025-10-24T11:04:53Z
dc.date.issued2021-03-30
dc.description.abstractSupplements for the paper entitled "Cross-lingual Visual Pre-training for Multimodal Machine Translation" which is accepted by the EACL'2021 conference. Further instructions on how to use these resources are explained at https://github.com/ImperialNLP/VTLM A tarball that contains a custom train, valid, test split of Conceptual Captions (CC) dataset. The included TSV files havean additional column containing automatic German translations of the original English captions. We only provide samples for which we could download the images and extract meaningful features. This amounts to ~3M out ouf ~3.3M original CC samples. A tarball of the exact object detector checkpoint used for feature extraction. A tarball with pre-extracted Multi30k dataset features.
dc.description.urihttp://hdl.handle.net/10044/1/106690
dc.description.urihttp://dx.doi.org/10.18653/v1/2021.eacl-main.112
dc.description.urihttps://doi.org/https://doi.org/10.18653/v1/2021.eacl-main.112
dc.identifier.openairededup_wf_002::562ed3fe7737976d9304c98df1427280
dc.identifier.urihttps://hdl.handle.net/20.500.14288/31142
dc.publisherZenodo
dc.rightsOPEN
dc.subjectmultimodal machine translation
dc.subjectimage captioning
dc.subjectmachine translation
dc.titleCross-lingual Visual Pre-training for Multimodal Machine Translation
dc.typeDataset
dspace.entity.typeData
local.import.sourceOpenAire

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