A Neural Network-Based Language Model for Automatic Poem Generation

The automatic generation of poems has been a persistent challenge in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence (AI) since the 1960s. Generating poetry involves understanding and creating various linguistic representations such as phonetics, syntax, semantics, meter, rhyme, and literary devices to produce structured and creative texts. This complexity makes it an intriguing area for AI research, prompting continuous innovation. Despite advancements in neural network-based language models, especially Large Language Models (LLMs), automatic poem generation remains difficult. This difficulty is attributed to the training data predominantly comprising prose, resulting in models that favor prose-like outputs. Consequently, specialized techniques are necessary to develop models that can consistently produce high-quality poems. This paper presents a neural network-based model designed specifically for automatic poem generation. The proposed model outperforms other language models in generating well-defined poems with metrical verses, demonstrating its enhanced capability and consistency in poem generation.