Design and Analysis of a Large Corpus of Post-Edited Translations: Quality Estimation, Failure Analysis and the Variability of Post-Edition

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Findings of the 2015 Workshop on Statistical Machine Translation (2015, positive): “…to generalize from a small amount of data; and ii ) even when they are not, the high variability of post-editing, already pointed out by Wisniewski et al. (2013), results in predicting legitimate corrections that have not been made by the annotators, therefore preventing from improving over the…”
Metrics for Evaluation of Word-level Machine Translation Quality Estimation (2016, background): “On the other hand, the actual borders of an error span in QE are not as important: the primary goal is to identify the erroneous region in the sentence, the task of ﬁnding the exact borders of an error cannot be solved unambiguously even by human annotators (Wisniewski et al., 2013).”
Online Multitask Learning for Machine Translation Quality Estimation (2015, methodology): “Post-editions were collected from four different translators, as described by Wisniewski et al. (2013).”
Why Predicting Post-Edition is so Hard? Failure Analysis of LIMSI Submission to the APE Shared Task (2015, result): “Several observations, similar to the ﬁndings of our analysis of an English-French post-editions corpus (Wisniewski et al., 2013 these two tables.”