  Recent neural models for data-to-document generation have achieved remarkable
progress in producing fluent and informative texts. However, large proportions
of generated texts do not actually conform to the input data. To address this
issue, we propose a new training framework which attempts to verify the
consistency between the generated texts and the input data to guide the
training process. To measure the consistency, a relation extraction model is
applied to check information overlaps between the input data and the generated
texts. The non-differentiable consistency signal is optimized via reinforcement
learning. Experimental results on a recently released challenging dataset
ROTOWIRE show improvements from our framework in various metrics.
