Parthasarathy, Venkatesh Balavadhani, Ramesh, Akshai, Haque, Rejwanul ORCID: 0000-0003-1680-0099 and Way, Andy ORCID: 0000-0001-5736-5930 (2020) The ADAPT system description for the WMT20 news translation task. In: Fifth Conference on Machine Translation (NEWS Shared Task), 19 -20 Nov 2020, Dominican Republic (Online).
Abstract
This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. monolingual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in low-resource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
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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 Institutes and Centres > ADAPT |
Published in: | Proceedings of the Fifrth Conference on Machine Translation. . Association for Computational Linguistics (ACL). |
Publisher: | Association for Computational Linguistics (ACL) |
Official URL: | https://www.aclweb.org/anthology/2020.wmt-1.27 |
Copyright Information: | © 2020 The Authors CC-BY-4.0 |
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, Science Foundation Ireland (SFI) under Grant Number 13/RC/2077 |
ID Code: | 25113 |
Deposited On: | 18 Nov 2020 16:56 by Rejwanul Haque . Last Modified 05 Dec 2023 15:22 |
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