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dc.contributor.authorStahlberg, Felix
dc.contributor.authorSaunders, Danielle
dc.contributor.authorByrne, William
dc.date.accessioned2018-10-10T05:20:24Z
dc.date.available2018-10-10T05:20:24Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283483
dc.description.abstractWe propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.
dc.rightsAll rights reserved
dc.titleAn Operation Sequence Model for Explainable Neural Machine Translation
dc.typeConference Object
prism.publicationNameEMNLP BlackboxNLP workshop 2018
dc.identifier.doi10.17863/CAM.30849
dcterms.dateAccepted2018-08-03
rioxxterms.versionofrecord10.17863/CAM.30849
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-08-03
dc.contributor.orcidStahlberg, Felix [0000-0002-0430-5704]
dc.contributor.orcidSaunders, Danielle [0000-0002-7943-3102]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.funder-project-idEPSRC (1632937)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L027623/1)
cam.issuedOnline2018-11-30
pubs.conference-nameEMNLP BlackboxNLP workshop 2018
pubs.conference-start-date2018-11-01
pubs.conference-finish-date2018-11-01


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