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

WS 2019 Ondřej DušekKarin SevegnaniIoannis KonstasVerena Rieser

We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings to individual outputs and to provide pairwise rankings of two different outputs. The latter is trained using pairwise hinge loss over scores from two copies of the rating network... (read more)

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