  Computer poetry generation is our first step towards computer writing.
Writing must have a theme. The current approaches of using sequence-to-sequence
models with attention often produce non-thematic poems. We present a novel
conditional variational autoencoder with a hybrid decoder adding the
deconvolutional neural networks to the general recurrent neural networks to
fully learn topic information via latent variables. This approach significantly
improves the relevance of the generated poems by representing each line of the
poem not only in a context-sensitive manner but also in a holistic way that is
highly related to the given keyword and the learned topic. A proposed augmented
word2vec model further improves the rhythm and symmetry. Tests show that the
generated poems by our approach are mostly satisfying with regulated rules and
consistent themes, and 73.42% of them receive an Overall score no less than 3
(the highest score is 5).
