Semantic Noise Matters for Neural Natural Language Generation

11/10/2019

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by   Ondřej Dušek, et al.

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Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97 omitting information, rather than hallucination.