Semantic Noise Matters for Neural Natural Language Generation

David M. Howcroft; Ond\v{r}ej Du\v{s}ek; Verena Rieser

Semantic Noise Matters for Neural Natural Language Generation

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arxiv 1911.03905 v1 pith:WX3D6JGI submitted 2019-11-10 cs.CL

Ondřej Dušek , David M. Howcroft , Verena Rieser This is my paper

classification cs.CL

keywords semanticfindgenerationlanguagenaturalneuralnnlgnoise

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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%, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.

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