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dc.contributor.authorChatterjee, Rajen
dc.contributor.authorArcan, Mihael
dc.contributor.authorNegri, Matteo
dc.contributor.authorTurchi, Marco
dc.identifier.citationChatterjee, Rajen, Arcan, Mihael, Negri, Matteo, & Turchi, Marco. (2016). Instance selection for online automatic post-editing in a multi-domain scenario. Paper presented at the The Twelfth Biennial Conference of the Association for Machine Translation in the Americas (AMTA 2016), Austin, Texas, 28 October - 01 November.en_IE
dc.description.abstractIn recent years, several end-to-end online translation systems have been proposed to successfully incorporate human post-editing feedback in the translation workflow. The performance of these systems in a multi-domain translation environment (involving different text genres, post-editing styles, machine translation systems) within the automatic post-editing (APE) task has not been thoroughly investigated yet. In this work, we show that when used in the APE framework the existing online systems are not robust towards domain changes in the incoming data stream. In particular, these systems lack in the capability to learn and use domain-specific post-editing rules from a pool of multi-domain data sets. To cope with this problem, we propose an online learning framework that generates more reliable translations with significantly better quality as compared with the existing online and batch systems. Our framework includes: i) an instance selection technique based on information retrieval that helps to build domain-specific APE systems, and ii) an optimization procedure to tune the feature weights of the log-linear model that allows the decoder to improve the post-editing quality.en_IE
dc.description.sponsorshipThis work has been partially supported by the EC-funded H2020 project QT21 (grant agreement no. 645452), and by the Science Foundation Ireland research grant (no. SFI/12/RC/2289).en_IE
dc.publisherAssociation for Machine Translation in the Americasen_IE
dc.relation.ispartofThe Twelfth Biennial Conference of the Association for Machine Translation in the Americas (AMTA 2016)en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.titleInstance selection for online automatic post-editing in a multi-domain scenarioen_IE
dc.typeConference Paperen_IE
dc.contributor.funderHorizon 2020en_IE
dc.contributor.funderScience Foundation Irelanden_IE
dc.local.contactMihael Arcan. Email:
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/645452/EU/QT21: Quality Translation 21/QT21en_IE
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE

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