  We aim to automatically generate natural language descriptions about an input
structured knowledge base (KB). We build our generation framework based on a
pointer network which can copy facts from the input KB, and add two attention
mechanisms: (i) slot-aware attention to capture the association between a slot
type and its corresponding slot value; and (ii) a new \emph{table position
self-attention} to capture the inter-dependencies among related slots. For
evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we
propose a KB reconstruction based metric by extracting a KB from the generation
output and comparing it with the input KB. We also create a new data set which
includes 106,216 pairs of structured KBs and their corresponding natural
language descriptions for two distinct entity types. Experiments show that our
approach significantly outperforms state-of-the-art methods. The reconstructed
KB achieves 68.8% - 72.6% F-score.

Computation and Language;Machine Learning