dc.contributor.author | Roman I. | en_US |
dc.contributor.author | Santana R. | en_US |
dc.contributor.author | Mendiburu A. | en_US |
dc.contributor.author | Lozano J.A. | en_US |
dc.date.accessioned | 2020-07-24T10:13:39Z | |
dc.date.available | 2020-07-24T10:13:39Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11824/1136 | |
dc.description.abstract | In 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.sponsorship | TIN2016-78365-R | en_US |
dc.format | application/pdf | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Learning and Intelligent Optimization | en_US |
dc.rights | Reconocimiento-NoComercial-CompartirIgual 3.0 España | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | en_US |
dc.subject | gaussian process | en_US |
dc.subject | kernels | en_US |
dc.subject | effort estimation | en_US |
dc.subject | evolutionary search | en_US |
dc.subject | genetic programming | en_US |
dc.title | Evolving Gaussian Process Kernels for Translation Editing Effort Estimation | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.relation.projectID | ES/1PE/SEV-2017-0718 | en_US |
dc.relation.projectID | EUS/BERC/BERC.2018-2021 | en_US |
dc.relation.projectID | EUS/ELKARTEK | en_US |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en_US |
dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | en_US |