R2D2: Robust Data-to-Text with Replacement Detection. (arXiv:2205.12467v1 [cs.CL])

Unfaithful text generation is a common problem for text generation systems.
In the case of Data-to-Text (D2T) systems, the factuality of the generated text
is particularly crucial for any real-world applications. We introduce R2D2, a
training framework that addresses unfaithful Data-to-Text generation by
training a system both as a generator and a faithfulness discriminator with
additional replacement detection and unlikelihood learning tasks. To facilitate
such training, we propose two methods for sampling unfaithful sentences. We
argue that the poor entity …

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