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Referential translation machines for quality estimation

Bicici, Ergun (2013) Referential translation machines for quality estimation. In: ACL 2013 8th workshop on statistical machine translation, 8-9 Aug 2013, Sofia, Bulgaria.

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We introduce 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 a reference corpus selected in the same domain, which can be used for estimating the quality of translation outputs, judging the semantic similarity between text, and evaluating the quality of student answers. 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. We develop novel techniques for solving all subtasks in the WMT13 quality estimation (QE) task (QET 2013) based on individual RTM models. Our results achieve improvements over last year’s QE task results (QET 2012), as well as our previous results, provide new features and techniques for QE, and rank 1st or 2nd in all of the subtasks.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Information retrieval
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
Publisher:Association for Computational Linguistics
Official URL:
Copyright Information:© 2013 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:QTLaunchPad, Centre for Next Generation Localisation
ID Code:19107
Deposited On:16 Aug 2013 12:09 by Mehmet Ergun Bicici. Last Modified 16 Aug 2013 12:09

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