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dc.contributor.authorRoman I.en_US
dc.contributor.authorSantana R.en_US
dc.contributor.authorMendiburu A.en_US
dc.contributor.authorLozano J.A.en_US
dc.date.accessioned2020-07-24T10:13:39Z
dc.date.available2020-07-24T10:13:39Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1136
dc.description.abstractIn many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, a text that has been translated using a machine translation method. Recent developments in this area have shown that Gaussian Processes can be accurate for post-editing effort prediction. However, the Gaussian Process kernel has to be chosen in advance, and this choice in- fluences the quality of the prediction. In this paper, we propose a Genetic Programming algorithm to evolve kernels for Gaussian Processes. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and achieves predictions that, in many cases, outperform those obtained with fixed kernels.en_US
dc.description.sponsorshipTIN2016-78365-Ren_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherLearning and Intelligent Optimizationen_US
dc.relationES/1PE/SEV-2017-0718en_US
dc.relationEUS/BERC/BERC.2018-2021en_US
dc.relationEUS/ELKARTEKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectgaussian processen_US
dc.subjectkernelsen_US
dc.subjecteffort estimationen_US
dc.subjectevolutionary searchen_US
dc.subjectgenetic programmingen_US
dc.titleEvolving Gaussian Process Kernels for Translation Editing Effort Estimationen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/acceptedVersionen_US


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