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。

LOAF-M2L: 歌词生成器的歌词和格式化的联合学习

LOAF-M2L: Joint Learning of Wording and Formatting for Singable  Melody-to-Lyric Generation

The use of Semantic Technologies - in particular the Semantic Web - has
revealed to be a great tool for describing the cultural heritage domain and
artistic practices. However, the panorama of ontologies for musicological
applications seems to be limited and restricted to specific applications. In
this research, we propose HaMSE, an ontology capable of describing musical
features that can assist musicological research. More specifically, HaMSE
proposes to address sues that have been affecting musicological research for
decades: the representation of music and the relationship between quantitative
and qualitative data. To do this, HaMSE allows the alignment between different
music representation systems and describes a set of musicological features that
can allow the music analysis at different granularity levels.

该研究提出了 HaMSE，一种语义技术下的本体论，可描述音乐特征，以解决音乐研究中的一些问题，包括音乐表现和数据定性的关系。