CONSTRAINT-AUGMENTED NEURAL NETWORKS FOR SHAKESPEAREAN VERSE: INTEGRATING PROSODIC FIDELITY WITH TRANSFORMER MODELS

This research proposes a methodology for augmenting prosodic constraints to enhance the creation of Shakespearean poetry in neural models of language. Contemporary transformer-based generators are proficient in fluency but often do not satisfy the stringent metrical and rhyming standards of formal poetry. To fill this gap, the proposed technique uses specialized prosody embeddings as well as prosody-aware attention mechanisms to combine explicit prosodic information such syllable counts, stress patterns, along with rhyme suffixes. The model is guided toward simultaneous optimization of linguistic quality as well as prosodic integrity by a composite training goal that combines cross-entropy loss with distinct meter and rhyme penalties and optional reinforcement learning. Experiments on a prosodically annotated Shakespearean corpus demonstrate substantial gains in meter accuracy, rhyme correctness, and overall stylistic adherence compared with standard GPT-based baselines, without sacrificing coherence or fluency. The results highlight the effectiveness of embedding phonological structure directly into neural architectures and offer a scalable approach for controllable poetic and stylistically constrained text generation.