As an essential step towards computer creativity, automatic poetry generation
has gained increasing attention these years. Though recent neural models make
prominent progress in some criteria of poetry quality, generated poems still
suffer from the problem of poor diversity. Related literature researches show
that different factors, such as life experience, historical background, etc.,
would influence composition styles of poets, which considerably contributes to
the high diversity of human-authored poetry. Inspired by this, we propose
MixPoet, a novel model that absorbs multiple factors to create various styles
and promote diversity. Based on a semi-supervised variational autoencoder, our
model disentangles the latent space into some subspaces, with each conditioned
on one influence factor by adversarial training. In this way, the model learns
a controllable latent variable to capture and mix generalized factor-related
properties. Different factor mixtures lead to diverse styles and hence further
differentiate generated poems from each other. Experiment results on Chinese
poetry demonstrate that MixPoet improves both diversity and quality against
three state-of-the-art models.

该研究提出了一种新方法 MixPoet，它通过半监督变分自编码器，将潜空间分解成多个子空间，并对每个子空间进行不同的因素训练，从而生成不同的诗歌风格以提高生成诗歌的多样性和质量，该方法在中国诗歌上的实验结果表明其相对于现有的三种最先进的模型具有更高的多样性和质量。