Let the Poem Hit the Rhythm: Using a Byte-Based Transformer for Beat-Aligned Poetry Generation
This work addresses a niche problem in computational creativity for poetry and music integration, but it is incremental as it builds on existing methods without major breakthroughs.
The paper tackled the problem of generating poetry that aligns with specific beat patterns, using a byte-based transformer model (ByT5) to achieve high beat alignment while maintaining semantic coherence.
The intersection between poetry and music provides an interesting case for computational creativity, yet remains relatively unexplored. This paper explores the integration of poetry and music through the lens of beat patterns, investigating whether a byte-based language model can generate words that fit specific beat patterns within the context of poetry. Drawing on earlier studies, we developed a method to train a byte-based transformer model, ByT5, to align poems with beat patterns. The results demonstrate a high level of beat alignment while maintaining semantic coherence. Future work will aim to improve the model's ability to create complete beat-aligned poems.