  As a precious part of the human cultural heritage, Chinese poetry has
influenced people for generations. Automatic poetry composition is a challenge
for AI. In recent years, significant progress has been made in this area
benefiting from the development of neural networks. However, the coherence in
meaning, theme or even artistic conception for a generated poem as a whole
still remains a big problem. In this paper, we propose a novel Salient-Clue
mechanism for Chinese poetry generation. Different from previous work which
tried to exploit all the context information, our model selects the most
salient characters automatically from each so-far generated line to gradually
form a salient clue, which is utilized to guide successive poem generation
process so as to eliminate interruptions and improve coherence. Besides, our
model can be flexibly extended to control the generated poem in different
aspects, for example, poetry style, which further enhances the coherence.
Experimental results show that our model is very effective, outperforming three
strong baselines.
