  Classical Chinese poetry is a jewel in the treasure house of Chinese culture.
Previous poem generation models only allow users to employ keywords to
interfere the meaning of generated poems, leaving the dominion of generation to
the model. In this paper, we propose a novel task of generating classical
Chinese poems from vernacular, which allows users to have more control over the
semantic of generated poems. We adapt the approach of unsupervised machine
translation (UMT) to our task. We use segmentation-based padding and
reinforcement learning to address under-translation and over-translation
respectively. According to experiments, our approach significantly improve the
perplexity and BLEU compared with typical UMT models. Furthermore, we explored
guidelines on how to write the input vernacular to generate better poems. Human
evaluation showed our approach can generate high-quality poems which are
comparable to amateur poems.
