  As an exquisite and concise literary form, poetry is a gem of human culture.
Automatic poetry generation is an essential step towards computer creativity.
In recent years, several neural models have been designed for this task.
However, among lines of a whole poem, the coherence in meaning and topics still
remains a big challenge. In this paper, inspired by the theoretical concept in
cognitive psychology, we propose a novel Working Memory model for poetry
generation. Different from previous methods, our model explicitly maintains
topics and informative limited history in a neural memory. During the
generation process, our model reads the most relevant parts from memory slots
to generate the current line. After each line is generated, it writes the most
salient parts of the previous line into memory slots. By dynamic manipulation
of the memory, our model keeps a coherent information flow and learns to
express each topic flexibly and naturally. We experiment on three different
genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both
automatic and human evaluation results show that our model outperforms current
state-of-the-art methods.
