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The ADAPT system description for the STAPLE 2020 English-to-Portuguese translation task

Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099, Moslem, Yasmin orcid logoORCID: 0000-0003-4595-6877 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) The ADAPT system description for the STAPLE 2020 English-to-Portuguese translation task. In: 4th Workshop on Neural Generation and Translation (WNGT 2020), 10 July 2020, Seattle, WA, USA (Online).

Abstract
This paper describes the ADAPT Centre’s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple n-best translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Computational linguistics
Computer Science > Computer engineering
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 Fourth Workshop on Neural Generation and Translation. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://www.aclweb.org/anthology/2020.ngt-1.17
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), 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) Grant Number 13/RC/2077.
ID Code:24562
Deposited On:25 Jun 2020 16:22 by Rejwanul Haque . Last Modified 11 May 2023 14:32
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