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dc.contributor.authorSu, Yixuan
dc.contributor.authorVandyke, D
dc.contributor.authorBaker, S
dc.contributor.authorWang, Y
dc.contributor.authorCollier, Nigel
dc.date.accessioned2022-05-27T23:30:31Z
dc.date.available2022-05-27T23:30:31Z
dc.date.issued2021-01-01
dc.identifier.isbn9781954085541
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/337572
dc.description.abstractParaphrase generation is an important and challenging NLG problem. In this work, we propose a new Identification-then-Aggregation (IA) framework to tackle this task. In the identification step, the input tokens are sorted into two groups by a novel Primary/Secondary Identification (PSI) algorithm. In the aggregation step, these groups are separately encoded, before being aggregated by a custom designed decoder, which autoregressively generates the paraphrased sentence. In extensive experiments on two benchmark datasets, we demonstrate that our model outperforms previous studies by a notable margin. We also show that the proposed approach can generate paraphrases in an interpretable and controllable way.
dc.rightsPublisher's own licence
dc.titleKeep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation
dc.typeConference Object
dc.publisher.departmentDepartment of Theoretical & Applied Linguistics
dc.publisher.departmentFaculty of Modern And Medieval Languages And Linguistics
dc.date.updated2022-05-27T06:31:56Z
prism.endingPage569
prism.publicationDate2021
prism.publicationNameFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
prism.startingPage560
dc.identifier.doi10.17863/CAM.84981
rioxxterms.versionofrecord10.17863/CAM.84981
rioxxterms.versionVoR
dc.contributor.orcidSu, Yixuan [0000-0002-1472-7791]
dc.contributor.orcidCollier, Nigel [0000-0002-7230-4164]
cam.orpheus.counter8*
cam.depositDate2022-05-27
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2100-01-01


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