  A major challenge in evaluating data-to-text (D2T) generation is measuring
the semantic accuracy of the generated text, i.e. checking if the output text
contains all and only facts supported by the input data. We propose a new
metric for evaluating the semantic accuracy of D2T generation based on a neural
model pretrained for natural language inference (NLI). We use the NLI model to
check textual entailment between the input data and the output text in both
directions, allowing us to reveal omissions or hallucinations. Input data are
converted to text for NLI using trivial templates. Our experiments on two
recent D2T datasets show that our metric can achieve high accuracy in
identifying erroneous system outputs.
