  Existing recurrent neural language models often fail to capture higher-level
structure present in text: for example, rhyming patterns present in poetry.
Much prior work on poetry generation uses manually defined constraints which
are satisfied during decoding using either specialized decoding procedures or
rejection sampling. The rhyming constraints themselves are typically not
learned by the generator. We propose an alternate approach that uses a
structured discriminator to learn a poetry generator that directly captures
rhyming constraints in a generative adversarial setup. By causing the
discriminator to compare poems based only on a learned similarity matrix of
pairs of line ending words, the proposed approach is able to successfully learn
rhyming patterns in two different English poetry datasets (Sonnet and Limerick)
without explicitly being provided with any phonetic information.
