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Computer poetry generation is our first step towards computer writing.Writing must have a theme. The current approaches of using sequence-to-sequencemodels with attention often produce non-thematic poems. We present a novelconditional variational autoencoder with a hybrid decoder adding thedeconvolutional neural networks to the general recurrent neural networks tofully learn topic information via latent variables. This approach significantlyimproves the relevance of the generated poems by representing each line of thepoem not only in a context-sensitive manner but also in a holistic way that ishighly related to the given keyword and the learned topic. A proposed augmentedword2vec model further improves the rhythm and symmetry. Tests show that thegenerated poems by our approach are mostly satisfying with regulated rules andconsistent themes, and 73.42% of them receive an Overall score no less than 3(the highest score is 5).

Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders