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Referential translation machines for predicting translation quality

Bicici, Ergun and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2014) Referential translation machines for predicting translation quality. In: ACL 2014 9th workshop on statistical machine translation, 26-27 June 2014, Baltimore, USA.

Abstract
We use referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts between any two data sets with respect to interpretants selected in the same domain, which are effective when making monolingual and bilingual similarity judgments. RTMs achieve top performance in automatic, accurate, and language independent prediction of sentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations and achieve the top performance in WMT13 quality estimation task (QET13). We improve our RTM models with the Parallel FDA5 instance selection model, with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT14 QET (QET14) subtask, obtain improvements over QET13 results, and rank $1$st in all of the tasks and subtasks of QET14.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
Computer Science > Machine learning
Computer Science > Artificial intelligence
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of ACL 2014 NINTH WORKSHOP ON STATISTICAL MACHINE TRANSLATION. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://www.statmt.org/wmt14/papers.html
Copyright Information:© 2014 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7 QTLaunchPad project, CNGL Centre for Global Intelligent Content, Science Foundation Ireland
ID Code:19990
Deposited On:11 Jul 2014 13:00 by Mehmet Ergun Bicici . Last Modified 09 Nov 2018 14:22
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