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dc.contributor.authorEscartin, Carla Parra
dc.contributor.authorBéchara, Hanna
dc.contributor.authorOrăsan, Constantin
dc.date.accessioned2017-06-22T10:42:18Z
dc.date.available2017-06-22T10:42:18Z
dc.date.issued2017-06-06
dc.identifier.citationCarla Parra Escartín, Hanna Béchara, Constantin Orăsan. Questing for Quality Estimation A User Study. The Prague Bulletin of Mathematical Linguistics, 108(1), pp. 343–354. doi: 10.1515/pralin-2017-0032.
dc.identifier.issn1804-0462
dc.identifier.doi10.1515/pralin-2017-0032
dc.identifier.urihttp://hdl.handle.net/2436/620521
dc.description.abstractPost-Editing of Machine Translation (MT) has become a reality in professional translation workflows. In order to optimize the management of projects that use post-editing and avoid underpayments and mistrust from professional translators, effective tools to assess the quality of Machine Translation (MT) systems need to be put in place. One field of study that could address this problem is Machine Translation Quality Estimation (MTQE), which aims to determine the quality of MT without an existing reference. Accurate and reliable MTQE can help project managers and translators alike, as it would allow estimating more precisely the cost of post-editing projects in terms of time and adequate fares by discarding those segments that are not worth post-editing (PE) and have to be translated from scratch. In this paper, we report on the results of an impact study which engages professional translators in PE tasks using MTQE. We measured translators? productivity in different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that QE information, when accurate, improves post-editing efficiency.
dc.description.sponsorshipEC
dc.language.isoen
dc.publisherde Gruyter
dc.relation.urlhttp://www.degruyter.com/view/j/pralin.2017.108.issue-1/pralin-2017-0032/pralin-2017-0032.xml
dc.subjectQuality Estimation
dc.subjectMachine Translation
dc.subjectPostediting
dc.titleQuesting for Quality Estimation A User Study
dc.typeJournal article
dc.identifier.journalThe Prague Bulletin of Mathematical Linguistics
dc.date.accepted2017-04-30
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUoW220617CO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0
rioxxterms.licenseref.startdate2017-06-22
dc.source.volume108
dc.source.issue1
dc.source.beginpage343
dc.source.endpage354
refterms.dateFCD2018-10-19T08:41:03Z
refterms.versionFCDVoR
refterms.dateFOA2017-06-22T00:00:00Z
html.description.abstractPost-Editing of Machine Translation (MT) has become a reality in professional translation workflows. In order to optimize the management of projects that use post-editing and avoid underpayments and mistrust from professional translators, effective tools to assess the quality of Machine Translation (MT) systems need to be put in place. One field of study that could address this problem is Machine Translation Quality Estimation (MTQE), which aims to determine the quality of MT without an existing reference. Accurate and reliable MTQE can help project managers and translators alike, as it would allow estimating more precisely the cost of post-editing projects in terms of time and adequate fares by discarding those segments that are not worth post-editing (PE) and have to be translated from scratch. In this paper, we report on the results of an impact study which engages professional translators in PE tasks using MTQE. We measured translators? productivity in different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that QE information, when accurate, improves post-editing efficiency.


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