  Recent advancements in data-to-text generation largely take on the form of
neural end-to-end systems. Efforts have been dedicated to improving text
generation systems by changing the order of training samples in a process known
as curriculum learning. Past research on sequence-to-sequence learning showed
that curriculum learning helps to improve both the performance and convergence
speed. In this work, we delve into the same idea surrounding the training
samples consisting of structured data and text pairs, where at each update, the
curriculum framework selects training samples based on the model's competence.
Specifically, we experiment with various difficulty metrics and put forward a
soft edit distance metric for ranking training samples. Our benchmarks show
faster convergence speed where training time is reduced by 38.7% and
performance is boosted by 4.84 BLEU.
