@inproceedings{chen-teufel-2024-scansion,
    title = "Scansion-based Lyrics Generation",
    author = "Chen, Yiwen  and
      Teufel, Simone",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1252",
    pages = "14370--14381",
    abstract = "We aim to generate lyrics for Mandarin songs with a good match between the melody and the tonal contour of the lyrics. Our solution relies on mBart, treating lyrics generation as a translation problem, but rather than translating directly from the melody as is common, our novelty in this paper is that we generate from scansion as an intermediate contour representation that can fit a given melody. One of the advantages of our solution is that it does not require a parallel melody-lyrics dataset. We also present a thorough automatic evaluation of our system against competitors, using several new evaluation metrics. These measure intelligibility, fit to melody, and use proxies for quantifying creativity (variation to other songs created by the same system in different settings, semantic similarity to keywords given to the system, perplexity). When comparing different implementations of scansion to competitor systems, a varied picture emerges. Our best system outperforms all others in lyric-melody fit and is in the top group of systems for two of the creativity metrics (variation and perplexity), overshadowing two large language models (LLM) specialised to this task.",
}

We aim to generate lyrics for Mandarin songs with a good match between the melody and the tonal contour of the lyrics. Our solution relies on mBart, treating lyrics generation as a translation problem, but rather than translating directly from the melody as is common, our novelty in this paper is that we generate from scansion as an intermediate contour representation that can fit a given melody. One of the advantages of our solution is that it does not require a parallel melody-lyrics dataset. We also present a thorough automatic evaluation of our system against competitors, using several new evaluation metrics. These measure intelligibility, fit to melody, and use proxies for quantifying creativity (variation to other songs created by the same system in different settings, semantic similarity to keywords given to the system, perplexity). When comparing different implementations of scansion to competitor systems, a varied picture emerges. Our best system outperforms all others in lyric-melody fit and is in the top group of systems for two of the creativity metrics (variation and perplexity), overshadowing two large language models (LLM) specialised to this task.
Chen, Yiwen Teufel, Simone
Scansion-based Lyrics Generation