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We present a recurrent neural network based system for automatic qualityestimation of natural language generation (NLG) outputs, which jointly learnsto assign numerical ratings to individual outputs and to provide pairwiserankings of two different outputs. The latter is trained using pairwise hingeloss over scores from two copies of the rating network.  We use learning to rank and synthetic data to improve the quality of ratingsassigned by our system: we synthesise training pairs of distorted systemoutputs and train the system to rank the less distorted one higher. This leadsto a 12% increase in correlation with human ratings over the previousbenchmark. We also establish the state of the art on the dataset of relativerankings from the E2E NLG Challenge (Du\v{s}ek et al., 2019), where syntheticdata lead to a 4% accuracy increase over the base model.

Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)