Analysing Data-To-Text Generation Benchmarks

Understand

Recently, several data-sets associating data to text have been created to train data-to-text surface realisers.

• It is unclear however to what extent the surface realisation task exercised by these data-sets is linguistically challenging.
• Do these data-sets provide enough variety to encourage the development of generic, high-quality data-to-text surface realisers ? In this paper, we argue that these data-sets have important drawbacks.
• We back up our claim using statistics, metrics and manual evaluation.