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dc.contributor.authorRei, M
dc.contributor.authorYannakoudakis, H
dc.date.accessioned2019-08-02T14:01:55Z
dc.date.available2019-08-02T14:01:55Z
dc.date.issued2016-08-12
dc.identifier.isbn9781510827585
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/295220
dc.description.abstract© 2016 Association for Computational Linguistics. In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators.
dc.language.isoen
dc.publisherThe Association for Computational Linguistics
dc.titleCompositional sequence labeling models for error detection in learner writing
dc.typeConference Object
prism.endingPage1191
prism.publicationDate2016
prism.publicationName54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers
prism.startingPage1181
prism.volume2
dc.identifier.doi10.17863/CAM.21364
rioxxterms.versionofrecord10.18653/v1/p16-1112
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-08-12
dc.contributor.orcidYannakoudakis, Helen [0000-0002-4429-7729]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.funder-project-idCambridge Assessment (unknown)
pubs.conference-nameThe 54th Annual Meeting of the Association for Computational Linguistics
pubs.conference-start-date2016-08-07
pubs.conference-finish-date2016-08-12


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