Despite previous efforts in melody-to-lyric generation research, there is
still a significant compatibility gap between generated lyrics and melodies,
negatively impacting the singability of the outputs. This paper bridges the
singability gap with a novel approach to generating singable lyrics by jointly
Learning wOrding And Formatting during Melody-to-Lyric training (LOAF-M2L).
After general-domain pretraining, our proposed model acquires length awareness
first from a large text-only lyric corpus. Then, we introduce a new objective
informed by musicological research on the relationship between melody and
lyrics during melody-to-lyric training, which enables the model to learn the
fine-grained format requirements of the melody. Our model achieves 3.75% and
21.44% absolute accuracy gains in the outputs' number-of-line and
syllable-per-line requirements compared to naive fine-tuning, without
sacrificing text fluency. Furthermore, our model demonstrates a 63.92% and
74.18% relative improvement of music-lyric compatibility and overall quality in
the subjective evaluation, compared to the state-of-the-art melody-to-lyric
generation model, highlighting the significance of formatting learning.

本文提出一种新的方法 LOAF-M2L 来生成旋律与歌词之间兼容的歌词，并引入音乐学研究中的信息来帮助模型学习旋律的细粒度格式要求，在主观评估中相对于现有最先进的旋律到歌词生成模型显示出 63.92％和 74.18％的相对改进。