Referential translation machines for predicting semantic similarity

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Findings of the 2016 Conference on Machine Translation (2016, background): “RTM (Task 1, Task 2, Task 3): Referential translation machines (RTMs) (Biçici and Way, 2015) are a language-independent approach for predicting translation quality, as well as for addressing other text similarity tasks.”
RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity (2017, methodology): “RTMs use ParFDA (Biçici, 2016a) for instance selection and machine translation performance prediction system (MTPPS) (Biçici and Way, 2015) for generating features for the training and the test set mapping both to the same space where the total number of features in each task becomes 368.”
Predicting Translation Performance with Referential Translation Machines (2017, methodology): “Evaluation metrics we use are Pearson’s correlation (r), mean absolute error (MAE), relative absolute error (RAE), MAER (mean absolute error relative), and MRAER (mean relative absolute error relative) (Biçici and Way, 2015).”
Referential Translation Machines for Predicting Translation Performance (2016, methodology): “RTMs are powerful enough to be applicable in different domains and tasks while achieving top performance in both monolingual (Bic¸ici and Way, 2015) and bilingual settings (Bic¸ici et al., 2015b).”