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dc.contributor.authorVulić, Ien
dc.contributor.authorKiela, Den
dc.contributor.authorClark, Stephenen
dc.contributor.authorMoens, MFen
dc.date.accessioned2017-09-12T12:01:29Z
dc.date.available2017-09-12T12:01:29Z
dc.date.issued2016-08-13en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/267177
dc.description.abstractRecent work has revealed the potential of using visual representations for bilingual lexicon learning (BLL). Such image-based BLL methods, however, still fall short of linguistic approaches. In this paper, we propose a simple yet effective multimodal approach that learns bilingual semantic representations that fuse linguistic and visual input. These new bilingual multi-modal embeddings display significant performance gains in the BLL task for three language pairs on two benchmarking test sets, outperforming linguistic-only BLL models using three different types of state-of-the-art bilingual word embeddings, as well as visual-only BLL models.
dc.description.sponsorshipThis work is supported by ERC Consolidator Grant LEXICAL (648909) and KU Leuven Grant PDMK/14/117. SC is supported by ERC Starting Grant DisCoTex (306920).
dc.publisherAssociation for Computational Linguistics
dc.titleMulti-Modal Representations for Improved Bilingual Lexicon Learningen
dc.typeConference Object
prism.endingPage194
prism.publicationDate2016en
prism.publicationNameProceedings of the 54th Annual Meeting of the Association for Computational Linguisticsen
prism.startingPage188
dc.identifier.doi10.17863/CAM.9719
dcterms.dateAccepted2016-08-07en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2016-08-13en
rioxxterms.typeConference Paper/Proceeding/Abstracten
dc.identifier.urlhttp://acl2016.org/en
pubs.conference-nameThe 54th Annual Meeting of the Association for Computational Linguisticsen
pubs.conference-start-date2016-08-07en


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