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Cited by 6Topic ModelingMusic Technology and Sound StudiesGenerative Adversarial Networks and Image Synthesis

Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning

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Ram, Naveen, Gummadi, Tanay, Bhethanabotla, Rahul, Savery, Richard J, Weinberg, Gil·2021·Proceedings of the 9th International Conference on Human-Agent Interaction
DOI 10.1145/3472307.3484175
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Abstract

Lyric generation is a popular sub-field of natural language generation that has seen growth in recent years. Pop lyrics are of unique interest due to the genre’s unique style and content, in addition to the high level of collaboration that goes on behind the scenes in the professional pop songwriting process. In this paper, we present a collaborative line-level lyric generation system that utilizes transfer-learning via the T5 transformer model, which, till date, has not been used to generate pop lyrics. By working and communicating directly with professional songwriters, we develop a model that is able to learn lyrical and stylistic tasks like rhyming, matching line beat requirements, and ending lines with specific target words. Our approach compares favorably to existing methods for multiple datasets and yields positive results from our online studies and interviews with industry songwriters.

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  • The theoretical framework may not fully describe Human-AI interaction in creative and professional writing.

    “While the framework is widely used in AI writing research ( [9,73,101]) and provided a valuable lens for this study, it may not fully describe Human-AI interaction in creative and professional writing.”

    From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [101]

    The citation is attached to the statement that a framework is widely used in AI writing research; the later qualification about its limits appears to be the citing authors’ own assessment. The Related Work section (p. 2) discusses lyric generation and language models, while the Discussion (p. 8) discusses human–AI collaboration. Neither passage identifies the theoretical framework or establishes its use across AI writing research (searched: theoretical framework, framework widely used). (LLM, conf=0.72)

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Research works printed but not linked: 24

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Cited by

Sorted by citations

Authors
Ram, N., Gummadi, T., Bhethanabotla, R., Savery, R. J., & Weinberg, G.
Affiliations
Georgia Institute of Technology (US)
ORCID
Venue
Proceedings of the 9th International Conference on Human-Agent Interaction
Publisher
ACM
Published
2021-11-09
Pages
165-173
DOI
10.1145/3472307.3484175
OpenAlex
W3211489172
Cited by
6
References
4
Citations by year
2026: 1 · 2025: 2 · 2024: 3
FWCI
0.35
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • International Conference on Human-Agent Interaction (HAI) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Music Technology and Sound Studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords
collaborative songwriting, transfer learning, T5 transformer, rhyme, songwriters, natural language generation

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