Neural Text Generation from Structured Data with Application to the Biography Domain

Understand

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.

• We experiment with a new dataset of biographies from Wikipedia that is an order of magnitude larger than existing resources with over 700k samples.
• The dataset is also vastly more diverse with a 400k vocabulary, compared to a few hundred words for Weathergov or Robocup.
• Our model builds upon recent work on conditional neural language model for text generation.