Quality estimation for translation selection

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Findings of the 2014 Workshop on Statistical Machine Translation (2014, methodology): “Previous work has looked into distinguishing human from machine translations (e.g. (Gamon et al., 2005)), but this problem setting is somehow artiﬁcial, and moreover arguably harder to solve nowadays given the higher general quality of current MT systems (Shah and Specia, 2014).”
SimpleNets: Quality Estimation with Resource-Light Neural Networks (2016, methodology): “Reliable solutions for QE can be useful in various tasks, such as improving post-editing efficiency (Specia, 2011), selecting high quality translations (Soricut and Echihabi, 2010), translation re-ranking (Shah and Specia, 2014), and visual assistance for manual translation revision (Bach et al.”
Multi-level Translation Quality Prediction with QuEst++ (2015, background): “…make MT more useful in a number of scenarios, for example, improving post-editing efﬁciency (Specia, 2011), selecting high quality segments (Soricut and Echihabi, 2010), selecting the best translation (Shah and Specia, 2014), and highlighting words or phrases that need revision (Bach et al., 2011).”
The JHU Parallel Corpus Filtering Systems for WMT 2018 (2018, background): “Shah and Specia (2014) described experiments on quality estimation which, given a source sentence, select the best translation among several options.”