Generating Safe Diversity in nlg via Imitation Learning
Table 3: Automatic evaluation results for cutting the tail methods and uniform sampling.
['[EMPTY]', 'Exact', 'DAgger', 'lols', 'Nucleus']
All the systems obtained comparable results for both performance and diversity based on automatic evaluation metrics suggesting that the benefits of a more broad and cautious exploration do not help the generation of higher quality texts. We conjecture that these results are due to the high quality of the expert policy which is able to provide a reliable and representative training signal for the diversity that the nlg is capable of producing correctly.