Language Adaptation for Extending Post-Editing Estimates for Closely Related Languages

This paper presents an open-source toolkit for predicting human post-editing efforts for closely related languages. At the moment, training resources for the Quality Estimation task are available for very few language directions and domains. Available resources can be expanded on the assumption that MT errors and the amount of post-editing required to correct them are comparable across related languages, even if the feature frequencies differ. In this paper we report a toolkit for achieving language adaptation, which is based on learning new feature representation using transfer learning methods. In particular, we report performance of a method based on Self-Taught Learning which adapts the English-Spanish pair to produce Quality Estimation models for translation from English into Portuguese, Italian and other Romance languages using the publicly available Autodesk dataset.