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Transductive data-selection algorithms for fine-tuning neural machine translation

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) Transductive data-selection algorithms for fine-tuning neural machine translation. In: The 8th Workshop on Patent and Scientific Literature Translation, Dublin, Ireland.

Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of the documents tend to perform better. Fine-tuning is a technique for adapting an NMT model to some domain. In this work, we want to use this technique to adapt the model to a given test set. In particular, we are using transductive data selection algorithms which take advantage the information of the test set to retrieve sentences from a larger parallel set.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:Data Selection;
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of The 8th Workshop on Patent and Scientific Literature Translation. . ACL Anthology.
Publisher:ACL Anthology
Official URL:https://www.aclweb.org/anthology/W19-7202
Copyright Information:© 2019 The Authors. Creative Commons 4.0 licence, no derivative works, attribution, CC-BY-ND.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI Research Centres Programme (Grant 13/RC/2106), European Regional Development Fund, European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 713567.
ID Code:24061
Deposited On:02 Jan 2020 09:52 by Alberto Poncelas . Last Modified 22 Jan 2021 14:23

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