J. Ramón Navarro-Cerdán

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Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different(More)
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