  Poetry generation has long been a challenge for artificial intelligence. In
the scope of Japanese poetry generation, many researchers have paid attention
to Haiku generation, but few have focused on Waka generation. To further
explore the creative potential of natural language generation systems in
Japanese poetry creation, we propose a novel Waka generation model, WakaVT,
which automatically produces Waka poems given user-specified keywords. Firstly,
an additive mask-based approach is presented to satisfy the form constraint.
Secondly, the structures of Transformer and variational autoencoder are
integrated to enhance the quality of generated content. Specifically, to obtain
novelty and diversity, WakaVT employs a sequence of latent variables, which
effectively captures word-level variability in Waka data. To improve linguistic
quality in terms of fluency, coherence, and meaningfulness, we further propose
the fused multilevel self-attention mechanism, which properly models the
hierarchical linguistic structure of Waka. To the best of our knowledge, we are
the first to investigate Waka generation with models based on Transformer
and/or variational autoencoder. Both objective and subjective evaluation
results demonstrate that our model outperforms baselines significantly.
