  In language generation models conditioned by structured data, the classical
training via maximum likelihood almost always leads models to pick up on
dataset divergence (i.e., hallucinations or omissions), and to incorporate them
erroneously in their own generations at inference. In this work, we build ontop
of previous Reinforcement Learning based approaches and show that a
model-agnostic framework relying on the recently introduced PARENT metric is
efficient at reducing both hallucinations and omissions. Evaluations on the
widely used WikiBIO and WebNLG benchmarks demonstrate the effectiveness of this
framework compared to state-of-the-art models.
