  Deep-learning models for language generation tasks tend to produce repetitive
output. Various methods have been proposed to encourage lexical diversity
during decoding, but this often comes at a cost to the perceived fluency and
adequacy of the output. In this work, we propose to ameliorate this cost by
using an Imitation Learning approach to explore the level of diversity that a
language generation model can reliably produce. Specifically, we augment the
decoding process with a meta-classifier trained to distinguish which words at
any given timestep will lead to high-quality output. We focus our experiments
on concept-to-text generation where models are sensitive to the inclusion of
irrelevant words due to the strict relation between input and output. Our
analysis shows that previous methods for diversity underperform in this
setting, while human evaluation suggests that our proposed method achieves a
high level of diversity with minimal effect to the output's fluency and
adequacy.
