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Post-editing effort of a novel with statistical and neural machine translation

Toral, Antonio ORCID: 0000-0003-2357-2960, Wieling, Martijn ORCID: 0000-0003-0434-1526 and Way, Andy ORCID: 0000-0001-5736-5930 (2018) Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5 . ISSN 2297-2668

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Abstract

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
Subjects:Computer Science > Machine learning
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:Frontiers Media
Official URL:http://dx.doi.org/10.3389/fdigh.2018.00009
Copyright Information:© 2018 The Authors. Open Access
Funders:Science Foundation Ireland (Grant 13/RC/2106), European Regional Development Fund
ID Code:24601
Deposited On:15 Jun 2020 12:17 by Vidatum Academic . Last Modified 18 Nov 2020 12:54

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