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.
Metadata
Item Type:
Article (Published)
Refereed:
Yes
Uncontrolled Keywords:
Neural MT; Statistical MT; Human MT evaluation; MOOCs
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