  Automatic melody-to-lyric generation is a task in which song lyrics are
generated to go with a given melody. It is of significant practical interest
and more challenging than unconstrained lyric generation as the music imposes
additional constraints onto the lyrics. The training data is limited as most
songs are copyrighted, resulting in models that underfit the complicated
cross-modal relationship between melody and lyrics. In this work, we propose a
method for generating high-quality lyrics without training on any aligned
melody-lyric data. Specifically, we design a hierarchical lyric generation
framework that first generates a song outline and second the complete lyrics.
The framework enables disentanglement of training (based purely on text) from
inference (melody-guided text generation) to circumvent the shortage of
parallel data.
  We leverage the segmentation and rhythm alignment between melody and lyrics
to compile the given melody into decoding constraints as guidance during
inference. The two-step hierarchical design also enables content control via
the lyric outline, a much-desired feature for democratizing collaborative song
creation. Experimental results show that our model can generate high-quality
lyrics that are more on-topic, singable, intelligible, and coherent than strong
baselines, for example SongMASS, a SOTA model trained on a parallel dataset,
with a 24% relative overall quality improvement based on human ratings.
