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Adaptation of machine translation models with back-translated data using transductive data selection methods

Poncelas, Alberto orcid logoORCID: 0000-0002-5089-1687, Maillette de Buy Wenniger, Gideon orcid logoORCID: 0000-0001-8427-7055 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2019) Adaptation of machine translation models with back-translated data using transductive data selection methods. In: A Proceedings of CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 7 - 13 Apr 2019, La Rochelle, France.

Abstract
Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent n-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding ngrams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science (LNCS) . Springer.
Publisher:Springer
Copyright Information:© 2019 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund., European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 713567.
ID Code:23870
Deposited On:21 Oct 2019 15:11 by Andrew Way . Last Modified 06 Jan 2022 17:45
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