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

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Large-scale machine translation evaluation of the iADAATPA project

Castilho, Sheila orcid logoORCID: 0000-0002-8416-6555, Resende, Natália orcid logoORCID: 0000-0002-5248-2457, Gaspari, Federico orcid logoORCID: 0000-0003-3808-8418, Way, Andy orcid logoORCID: 0000-0001-5736-5930, O'Dowd, Tony, Mazur, Marek, Herranz, Manuel, Helle, Alex, Ramirez-Sanchez, Gema, Sanchez-Cartagena, Victor, Pinnis, Marcis and Sics, Valters (2019) Large-scale machine translation evaluation of the iADAATPA project. In: MT Summit XVII, 19-23 Aug 2019, Dublin, Ireland.

This paper reports the results of an indepth evaluation of 34 state-of-the-art domain-adapted machine translation (MT) systems that were built by four leading MT companies as part of the EU-funded iADAATPA project. These systems support a wide variety of languages for several domains. The evaluation combined automatic metrics and human methods, namely assessments of adequacy, fluency, and comparative ranking. The paper also discusses the most effective techniques to build domain-adapted MT systems for the relevant language combinations and domains.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects: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: Forcada, Mikel, Way, Andy, Tinsley, John, Shterionov, Dimitar, Rici, Celia and Gaspari, Federico, (eds.) Proceedings of MT Summit XVII. 2. European Association for Machine Translation.
Publisher:European Association for Machine Translation
Official URL:https://www.aclweb.org/anthology/W19-6732.pdf
Copyright Information:© 2018 The authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:INEA through grant N ◦ 2016-EU-IA-0132 as part of the EU’s CEF Telecom Programme, ADAPT Centre for Digital Content Technology at Dublin City University is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/ 2106) and is co-funded under the European Regional Development Fund.
ID Code:23863
Deposited On:21 Oct 2019 12:30 by Andrew Way . Last Modified 20 Jan 2021 16:41

Full text available as:

[thumbnail of W19-6732.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