Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

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: Fourth Workshop on Neural Generation and Translation (WNGT), Association for Computational Linguistics (ACL), 10 July 2020, Seattle, WA, USA (Online).

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 nbest translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Additional Information:Video: https://slideslive.com/38929831/the-adapt-system-description-for-the-staple-2020-englishtoportuguese-translation-task
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 Institutes and Centres > ADAPT
Published in: Proceedings of the Fourth Workshop on Neural Generation and Translation (WNGT). .
Official URL:http://dx.doi.org/10.18653/v1/2020.ngt-1.17
Copyright Information:© 2020 The Authors. CC-BY-4.0
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106), European Regional Development Fund, Research grants from SFI and Microsoft under Grant Numbers 13/RC/2077 and 18/CRT/6224
ID Code:25447
Deposited On:28 Jan 2021 14:12 by Thomas Murtagh . Last Modified 28 Jan 2021 14:12

Full text available as:

[thumbnail of 2020.ngt-1.17.pdf]
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Downloads per month over past year

Archive Staff Only: edit this record