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Evaluating MT for massive open online courses: a multifaceted comparison between PBSMT and NMT systems

Castilho, Sheila ORCID: 0000-0002-8416-6555, Moorkens, Joss ORCID: 0000-0003-0766-0071, Gaspari, Federico ORCID: 0000-0003-3808-8418, Sennrich, Rico, Way, Andy ORCID: 0000-0001-5736-5930 and Georgakopoulou, Panayota (2018) Evaluating MT for massive open online courses: a multifaceted comparison between PBSMT and NMT systems. Machine Translation, 32 (3). pp. 255-278. ISSN 0922-6567

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Abstract

This article reports a multifaceted comparison between statistical and neural machine translation (MT) systems that were developed for translation of data from Massive Open Online Courses (MOOCs). The study uses four language pairs: English to German, Greek, Portuguese, and Russian. Translation quality is evaluated using automatic metrics and human evaluation, carried out by professional translators. Results show that neural MT is preferred in side-by-side ranking, and is found to contain fewer overall errors. Results are less clear-cut for some error categories, and for temporal and technical post-editing effort. In addition, results are reported based on sentence length, showing advantages and disadvantages depending on the particular language pair and MT paradigm.

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Neural MT; Statistical MT; Human MT evaluation; MOOCs
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies
Research Initiatives and Centres > ADAPT
Publisher:Springer
Official URL:https://doi.org/10.1007%2Fs10590-018-9221-y
Copyright Information:© 2018 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:TraMOOC project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No644333, Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund
ID Code:23076
Deposited On:11 Mar 2019 16:57 by Thomas Murtagh . Last Modified 20 Jan 2021 16:36

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