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The ADAPT’s submissions to the WMT20 biomedical translation task

Nayak, Prashanth, Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) The ADAPT’s submissions to the WMT20 biomedical translation task. In: The Fifth Conference on Machine Translation (The Biomedical Shared Task), 19-20 Nov 2020, Dominican Republic (Online).

Abstract
This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task for English-to-Basque. We present the machine translation (MT) systems that were built to translate scientific abstracts and terms from biomedical terminologies, and using the state-of-the-art neural MT (NMT) model: Transformer. In order to improve our baseline NMT system, we employ a number of methods, e.g. “pseudo” parallel data selection, monolingual data selection for synthetic corpus creation, mining monolingual sentences for adapting our NMT systems to this task, hyperparameters search for Transformer in low-resource scenarios. Our experiments show that systematic addition of the aforementioned techniques to the baseline yields an excellent performance in the English-to-Basque translation task.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Computational linguistics
Computer Science > Machine learning
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 the Fifth Conference on Machine Translation (Shared Task Papers). . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://aclanthology.org/2020.wmt-1.91.pdf
Copyright Information:© 2020 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 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:25107
Deposited On:22 Oct 2020 16:22 by Rejwanul Haque . Last Modified 06 Jan 2022 17:52
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