Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text
  Generation
Mateusz Lango Ondřej Dušek
Hallucination of text ungrounded in the input is a well-known problem in
neural data-to-text generation. Many methods have been proposed to mitigate it,
but they typically require altering model architecture or collecting additional
data, and thus cannot be easily applied to an existing model. In this paper, we
explore a new way to mitigate hallucinations by combining the probabilistic
output of a generator language model (LM) with the output of a special "text
critic" classifier, which guides the generation by assessing the match between
the input data and the text generated so far. Our method does not need any
changes to the underlying LM's architecture or training procedure and can thus
be combined with any model and decoding operating on word probabilities. The
critic does not need any additional training data, using the base LM's training
data and synthetic negative examples. Our experimental results show that our
method improves over the baseline on the WebNLG and OpenDialKG benchmarks.