  Poetry generation has been a difficult task in natural language processing.
Unlike plain neural text generation tasks, poetry has a high requirement for
novelty, since an easily-understood sentence with too many high frequency words
might not be considered as poetic, while adequately ambiguous sentences with
low frequency words can possibly be novel and creative. Inspired by this, we
present Lingxi, a diversity-aware Chinese modern poetry generation system. We
propose nucleus sampling with randomized head (NS-RH) algorithm, which
randomizes the high frequency part ("head") of the predicted distribution, in
order to emphasize on the "comparatively low frequency" words. The proposed
algorithm can significantly increase the novelty of generated poetry compared
with traditional sampling methods. The permutation of distribution is
controllable by tuning the filtering parameter that determines the "head" to
permutate, achieving diversity-aware sampling. We find that even when a large
portion of filtered vocabulary is randomized, it can actually generate fluent
poetry but with notably higher novelty. We also propose a
semantic-similarity-based rejection sampling algorithm, which creates longer
and more informative context on the basis of the short input poetry title while
maintaining high semantic similarity to the title, alleviating the off-topic
issue.
