  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 retrieval capability of D2T systems is one of the
primary sources of unfaithfulness, so in addition to the existing metrics, we
further propose NER-based metrics to evaluate the fidelity of D2T generations.
Our experimental results show that R2D2 systems could effectively mitigate the
unfaithful text generation, and they achieve new state-of-the-art results on
FeTaQA, LogicNLG, and ToTTo, all with significant improvements.

Computation and Language