Generating Safe Diversity in nlg via Imitation Learning
Table 4: Human Evaluation results.
['[EMPTY]', 'Fluency  [ITALIC] raw', 'Fluency  [ITALIC] z-score', 'Adequacy  [ITALIC] raw', 'Adequacy  [ITALIC] z-score']
We sampled 1500 texts from each of the different mcd-nlg configurations, Nucleus sampling and greedy inference. We include the latter to provide a benchmark for the other models. To determine whether the observed differences were statistically significant we used the Wilcoxon rank sum test.