Cross-Cultural Poetry Rewriting Model: Style Transfer and Cultural Transformation in Generative AI

This project explores cross-cultural style control in generative AI, dedicated to enabling intelligent rewriting of poetry across different cultures while preserving the core emotions and imagery of the original work, demonstrating innovative applications of AI in literary creation and cultural exchange. It challenges the stereotype of AI handling structured data, explores the possibility of machines understanding poetry and transforming cultural differences, and opens up new paths for collaborative artistic creation between humans and machines.

Style transfer has long been a challenge in the field of NLP: discrete text symbols make gradient propagation optimization difficult; style and content are closely intertwined, making style transfer while preserving semantic consistency complex; poetic style involves deep cultural backgrounds, metaphorical systems, and aesthetic habits. Cross-cultural poetry rewriting further requires the model to understand the poetic traditions of two cultures, achieving multi-level transformations in language, rhythm, imagery, etc., while preserving the emotional core, pushing the challenge to a new level.

The project may adopt the following technical routes:

• Transformer-based sequence generation: Using self-attention mechanisms to capture long-range dependencies in poetry;
• Style decoupling and conditional generation: Decomposing content and style representations via VAE, GAN, or diffusion models to achieve style control;
• Cross-language and cross-cultural transfer learning: General text pre-training + fine-tuning on specific poetry, combined with multilingual alignment techniques;
• Reinforcement learning and human feedback: Learning human subjective judgments on poetry quality via RLHF.

The model has broad application prospects:

• Literary education: Helping students compare the relationship between poetic styles and content across different cultures;
• Cultural exchange: Providing a non-translational way to appreciate foreign poetry;
• Creative writing assistance: Showing poets cross-cultural expressions of the same theme;
• Comparative literature research: Assisting scholars in systematically analyzing the characteristics and transformation laws of cultural poetic traditions.

The project faces many challenges:

• Cultural sensitivity: Automatic transformation may distort cultural connotations leading to misinterpretation;
• Quality assessment: The subjectivity of poetic aesthetics is difficult to measure with objective indicators;
• Originality and copyright: Issues of copyright ownership for rewritten copyrighted poems;
• Risk of cultural homogenization: Over-reliance on AI may weaken cultural diversity. It is necessary to advance technical and ethical thinking in parallel.

Project Significance: Represents the cutting edge of the intersection between AI and humanities, demonstrating the potential of machine learning in creative tasks, and exploring fundamental issues such as machine understanding of poetry and transformation of cultural differences. Future Outlook: The results are expected to extend to broader cultural transformation tasks such as cross-cultural story rewriting, stylized machine translation, cross-cultural dialogue generation, and multicultural fusion creation.

This project does not aim to replace human poets, but to explore the possibilities of collaborative artistic creation between humans and machines. It breaks down cultural barriers through cross-cultural poetry rewriting and highlights the common emotional experiences of humanity. The ultimate goal of technology is to expand the boundaries of human expression and understanding; at the intersection of AI and humanities, new possibilities are constantly emerging.