  We present a comparison of word-based and character-based
sequence-to-sequence models for data-to-text natural language generation, which
generate natural language descriptions for structured inputs. On the datasets
of two recent generation challenges, our models achieve comparable or better
automatic evaluation results than the best challenge submissions. Subsequent
detailed statistical and human analyses shed light on the differences between
the two input representations and the diversity of the generated texts. In a
controlled experiment with synthetic training data generated from templates, we
demonstrate the ability of neural models to learn novel combinations of the
templates and thereby generalize beyond the linguistic structures they were
trained on.
