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1、Multi-Way,MultilingualNeuralMachineTranslationwithaSharedAttentionMechanismOrhanFiratKyunghyunChoYoshuaBengioMiddleEastTechnicalUniversityNewYorkUniversityUniversityofMontrealorhan.firat@ceng.metu.edu.trCIFARSeniorFellowAbstractintoapointinacontinuousvectorspace,resultinginafixed-di
2、mensionalcontextvector.Theotherre-Weproposemulti-way,multilingualneuralcurrentneuralnetwork,calledadecoder,thengener-machinetranslation.Theproposedapproachatesatargetsequenceagainofvariablelengthstart-enablesasingleneuraltranslationmodeltoingfromthecontextvector.Thisapproachhowever
3、translatebetweenmultiplelanguages,withanumberofparametersthatgrowsonlylin-hasbeenfoundtobeinefficientin(Choetal.,2014a)earlywiththenumberoflanguages.Thiswhenhandlinglongsentences,duetothedifficultyismadepossiblebyhavingasingleatten-inlearningacomplexmappingbetweenanarbitrarytionmecha
4、nismthatissharedacrossalllan-longsentenceandasinglefixed-dimensionalvector.guagepairs.Wetraintheproposedmulti-In(Bahdanauetal.,2014),aremedytothisissueway,multilingualmodelontenlanguagepairswasproposedbyincorporatinganattentionmecha-fromWMT’15simultaneouslyandobservenismtothebasicen
5、coder-decodernetwork.Theat-clearperformanceimprovementsovermodelstrainedononlyonelanguagepair.Inpartic-tentionmechanismintheencoder-decodernetworkular,weobservethattheproposedmodelsig-freesthenetworkfromhavingtomapasequenceofnificantlyimprovesthetranslationqualityofarbitrarylengthto
6、asingle,fixed-dimensionalvec-low-resourcelanguagepairs.tor.Sincethisattentionmechanismwasintroducedtotheencoder-decodernetworkformachinetrans-1Introductionlation,neuralmachinetranslation,whichispurelybasedonneuralnetworkstoperformfullend-to-endNeuralMachineTranslationIthasbeenshownt
7、ranslation,hasbecomecompetitivewiththeexist-thatadeep(recurrent)neuralnetworkcansuccess-ingphrase-basedstatisticalmachinetranslationinfullylearnacomplexmappingbetweenvariable-arXiv:1601.01073v1[cs.CL]6Jan2016manylanguagepairs(Jeanetal.,2015;Gulcehreetlengthinputandoutputsequenceson
8、itsown.Someal.,2015;Luonge
