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.

R2D2 是一种数据到文本生成的训练框架，它通过训练系统作为生成器和忠诚度鉴别器，加上替换检测和非似然性学习任务，来解决不忠实的数据到文本生成问题。我们还提出了两种采样不忠实句子的方法和 NER 基于的指标来评估 D2T 生成的保真度。实验结果表明，R2D2 系统能够有效缓解不忠实的文本生成，并在 FeTaQA、LogicNLG 和 ToTTo 上取得了新的最佳效果。