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A human evaluation of English-Irish statistical and neural machine translation

Dowling, Meghan orcid logoORCID: 0000-0003-1637-4923, Castilho, Sheila orcid logoORCID: 0000-0002-8416-6555, Moorkens, Joss orcid logoORCID: 0000-0003-4864-5986, Lynn, Teresa and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) A human evaluation of English-Irish statistical and neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 6 Nov 2020, Lisbon, Portugal.

Abstract
With official status in both Ireland and the EU, there is a need for high-quality English-Irish (EN-GA) machine translation (MT) systems which are suitable for use in a professional translation environment. While we have seen recent research on improving both statistical MT and neural MT for the EN-GA pair, the results of such systems have always been reported using automatic evaluation metrics. This paper provides the first human evaluation study of EN-GA MT using professional translators and in-domain (public administration) data for a more accurate depiction of the translation quality available via MT.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:neural machine translation; statistical machine translation; human evaluation; machine translation post-editing; Irish language
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
Humanities > Translating and interpreting
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 22nd Annual Conference of the European Association for Machine Translation. . European Association for Machine Translation.
Publisher:European Association for Machine Translation
Official URL:https://www.aclweb.org/anthology/2020.eamt-1.46
Copyright Information:© 2020 The Authors. (CC-BY-ND-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 through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund through Grant #13/RC/2106.
ID Code:24418
Deposited On:30 Apr 2020 11:30 by Meghan Dowling . Last Modified 10 Mar 2021 12:06
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