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Bridging languages through images with deep partial canonical correlation analysis

dc.contributor.authorRotman, G
dc.contributor.authorVulić, I
dc.contributor.authorReichart, R
dc.date.accessioned2018-09-10T22:18:13Z
dc.date.available2018-09-10T22:18:13Z
dc.date.issued2018
dc.description.abstractWe present a deep neural network that leverages images to improve bilingual text embeddings. Relying on bilingual image tags and descriptions, our approach conditions text embedding induction on the shared visual information for both languages, producing highly correlated bilingual embeddings. In particular, we propose a novel model based on Partial Canonical Correlation Analysis (PCCA). While the original PCCA finds linear projections of two views in order to maximize their canonical correlation conditioned on a shared third variable, we introduce a non-linear Deep PCCA (DPCCA) model, and develop a new stochastic iterative algorithm for its optimization. We evaluate PCCA and DPCCA on multilingual word similarity and cross-lingual image description retrieval. Our models outperform a large variety of previous methods, despite not having access to any visual signal during test time inference. Our code and data are available at: https://github.com/rotmanguy/DPCCA}
dc.identifier.doi10.17863/CAM.27464
dc.identifier.isbn9781948087322
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/280100
dc.language.isoeng
dc.publisherAssociation for Computational Linguistics
dc.publisher.urlhttp://dx.doi.org/10.18653/v1/p18-1084
dc.titleBridging languages through images with deep partial canonical correlation analysis
dc.typeConference Object
dcterms.dateAccepted2018-04-21
prism.endingPage921
prism.publicationDate2018
prism.publicationNameACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
prism.startingPage910
prism.volume1
pubs.conference-finish-date2018-07
pubs.conference-nameProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
pubs.conference-start-date2018-07
pubs.funder-project-idEuropean Research Council (648909)
rioxxterms.licenseref.startdate2018-01-01
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeConference Paper/Proceeding/Abstract
rioxxterms.versionAM
rioxxterms.versionofrecord10.18653/v1/p18-1084

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