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Quality expectations of machine translation

Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2018) Quality expectations of machine translation. In: Moorkens, Joss orcid logoORCID: 0000-0003-4864-5986, Castilho, Sheila orcid logoORCID: 0000-0002-8416-6555, Gaspari, Federico orcid logoORCID: 0000-0003-3808-8418 and Doherty, Stephen orcid logoORCID: 0000-0003-0887-1049, (eds.) Translation Quality Assessment: From Principles to Practice. Machine Translation: Technologies and Applications Series Volume, 1 . Springer, Berlin/Heidelberg, pp. 159-178. ISBN 978-3-319-91240-0

Abstract
Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself.
Metadata
Item Type:Book Section
Refereed:Yes
Uncontrolled Keywords:Translation quality assessment ;Translation metrics; Neural machine translation;Translator productivity;Translation users
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
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/978-3-319-91241-7_8
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 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:24600
Deposited On:06 Jul 2020 11:09 by Vidatum Academic . Last Modified 24 Jul 2020 11:03
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