  The generation of lyrics tightly connected to accompanying melodies involves
establishing a mapping between musical notes and syllables of lyrics. This
process requires a deep understanding of music constraints and semantic
patterns at syllable-level, word-level, and sentence-level semantic meanings.
However, pre-trained language models specifically designed at the syllable
level are publicly unavailable. To solve these challenging issues, we propose
to exploit fine-tuning character-level language models for syllable-level
lyrics generation from symbolic melody. In particular, our method endeavors to
incorporate linguistic knowledge of the language model into the beam search
process of a syllable-level Transformer generator network. Additionally, by
exploring ChatGPT-based evaluation for generated lyrics, along with human
subjective evaluation, we demonstrate that our approach enhances the coherence
and correctness of the generated lyrics, eliminating the need to train
expensive new language models.
