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dc.contributor.authorGlavaš, G
dc.contributor.authorVulić, I
dc.date.accessioned2019-03-14T00:30:31Z
dc.date.available2019-03-14T00:30:31Z
dc.date.issued2019
dc.identifier.isbn9783030157111
dc.identifier.issn0302-9743
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/290531
dc.description.abstractWe present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint multilingual space and learns to distinguish true translation pairs from semantically related sentences across languages. The proposed model combines a recurrent sequence encoder with a bidirectional attention layer and an intra-sentence attention mechanism. This way the final fixed-size sentence representations in each training sentence pair depend on the selection of contextualized token representations from the other sentence. The representations of both sentences are then combined using the bilinear product function to predict the relevance score. We show that, coupled with a shared multilingual word embedding space, the proposed model strongly outperforms unsupervised cross-lingual ranking functions, and that further boosts can be achieved by combining the two approaches. Most importantly, we demonstrate the model's effectiveness in zero-shot language transfer settings: our multilingual framework boosts cross-lingual sentence retrieval performance for unseen language pairs without any training examples. This enables robust cross-lingual sentence retrieval also for pairs of resource-lean languages, without any parallel data.
dc.publisherSpringer International Publishing
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleZero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model
dc.typeConference Object
prism.endingPage538
prism.publicationDate2019
prism.publicationNameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
prism.startingPage523
prism.volume11437 LNCS
dc.identifier.doi10.17863/CAM.37761
dcterms.dateAccepted2018-12-10
rioxxterms.versionofrecord10.1007/978-3-030-15712-8_34
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-01-01
dc.identifier.eissn1611-3349
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.funder-project-idEuropean Research Council (648909)
cam.issuedOnline2019-04-07
pubs.conference-nameProceedings of the 41st European Conference on Information Retrieval (ECIR 2019)
pubs.conference-start-date2019-04-14
pubs.conference-finish-date2019-04-18
rioxxterms.freetoread.startdate2020-04-15


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