Automatic song writing is a topic of significant practical interest. However,
its research is largely hindered by the lack of training data due to copyright
concerns and challenged by its creative nature. Most noticeably, prior works
often fall short of modeling the cross-modal correlation between melody and
lyrics due to limited parallel data, hence generating lyrics that are less
singable. Existing works also lack effective mechanisms for content control, a
much desired feature for democratizing song creation for people with limited
music background. In this work, we propose to generate pleasantly listenable
lyrics without training on melody-lyric aligned data. Instead, we design a
hierarchical lyric generation framework that disentangles training (based
purely on text) from inference (melody-guided text generation). At inference
time, we leverage the crucial alignments between melody and lyrics and compile
the given melody into constraints to guide the generation process. Evaluation
results show that our model can generate high-quality lyrics that are more
singable, intelligible, coherent, and in rhyme than strong baselines including
those supervised on parallel data.

本文提出了一种基于分层框架和旋律 - 歌词对齐的歌词生成模型，能够在没有旋律 - 歌词对齐数据的情况下，通过对内容进行控制生成更可唱、更易懂、更连贯和有韵律的高质量歌词。