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A review of the state‑of‑the‑art in automatic post‑editing

do Carmo, Félix orcid logoORCID: 0000-0003-4193-3854, Shterionov, Dimitar orcid logoORCID: 0000-0001-6300-797X, Moorkens, Joss orcid logoORCID: 0000-0003-0766-0071, Wagner, Joachim orcid logoORCID: 0000-0002-8290-3849, Hossari, Murhaf, Paquin, Eric, Schmidtke, Dag, Groves, Declan and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) A review of the state‑of‑the‑art in automatic post‑editing. Machine Translation, 35 (2). pp. 101-143. ISSN 0922-6567

Abstract
This article presents a review of the evolution of automatic post-editing, a term that describes methods to improve the output of machine translation systems, based on knowledge extracted from datasets that include post-edited content. The article describes the specificity of automatic post-editing in comparison with other tasks in machine translation, and it discusses how it may function as a complement to them. Particular detail is given in the article to the five-year period that covers the shared tasks presented in WMT conferences (2015–2019). In this period, discussion of automatic post-editing evolved from the definition of its main parameters to an announced demise, associated with the difficulties in improving output obtained by neural methods, which was then followed by renewed interest. The article debates the role and relevance of automatic post-editing, both as an academic endeavour and as a useful application in commercial workflows.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Automatic Post-editing; Neural Post-editing; Neural machine translation; State-of-the-art in Automatic Post-editing
Subjects:Computer Science > Machine translating
Humanities > Translating and interpreting
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:http://dx.doi.org/10.1007/s10590-020-09252-y
Copyright Information:© 2020 The Authors.
Funders:ADAPT Centre for Digital Content Technology funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund, Science Foundation Ireland (SFI) under Grant Number 13/RC/2077., European Union’s Horizon 2020 research and innovation programme, under the EDGE COFUND Marie Skłodowska-Curie Grant Agreement No. 713567.
ID Code:25316
Deposited On:06 Jan 2021 13:04 by Joss Moorkens . Last Modified 11 May 2023 14:00
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