Post-editing effort of a novel with statistical and neural machine
translation
Toral, Antonio, Wieling, Martijn and Way, AndyORCID: 0000-0001-5736-5930
(2018)
Post-editing effort of a novel with statistical and neural machine
translation.
Frontiers in Digital Humanities, 5
(9).
pp. 1-11.
ISSN 2297-2668
We conduct the first experiment in the literature in which a novel is translated
automatically and then post-edited by professional literary translators. Our case study
is Warbreaker, a popular fantasy novel originally written in English, which we translate
into Catalan. We translated one chapter of the novel (over 3,700 words, 330 sentences)
with two data-driven approaches to Machine Translation (MT): phrase-based statistical
MT (PBMT) and neural MT (NMT). Both systems are tailored to novels; they are trained
on over 100 million words of fiction. In the post-editing experiment, six professional
translators with previous experience in literary translation translate subsets of this chapter
under three alternating conditions: from scratch (the norm in the novel translation
industry), post-editing PBMT, and post-editing NMT. We record all the keystrokes, the
time taken to translate each sentence, as well as the number of pauses and their duration.
Based on these measurements, and using mixed-effects models, we study post-editing
effort across its three commonly studied dimensions: temporal, technical and cognitive.
We observe that both MT approaches result in increases in translation productivity: PBMT
by 18%, and NMT by 36%. Post-editing also leads to reductions in the number of
keystrokes: by 9% with PBMT, and by 23% with NMT. Finally, regarding cognitive effort,
post-editing results in fewer (29 and 42% less with PBMT and NMT, respectively) but
longer pauses (14 and 25%).
Item Type:
Article (Published)
Refereed:
Yes
Additional Information:
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fdigh.
2018.00009/full#supplementary-material