How to Make Neural Natural Language Generation as Reliable as Templates in Task Oriented Dialogue
EMNLP 2020, pp. 2877-2888, 2020.
Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiment...More
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