Imagine having access to countless translations of your favorite literary masterpiece, each offering unique insights into the author's intent and cultural nuances. This dream is becoming a reality thanks to advancements in machine translation (MT). Researchers are exploring how multiple reference translations can enhance the quality and depth of literary MT, moving beyond single-reference training models. They've discovered that simply throwing more translated texts at an AI isn't enough. The secret lies in understanding the subtle, meaningful variations between those translations. This innovative approach uses a clever system to classify the semantic similarity between different English translations of world literature. They found that focusing on translations with 'medium' semantic similarity, those with interesting yet faithful interpretations, significantly improves the AI's ability to produce nuanced and accurate translations. While adding more source texts with single references did improve performance slightly, using multiple references with varying degrees of similarity proved more effective overall. Interestingly, translations with 'low' semantic similarity, representing more divergent interpretations, hindered the AI's learning process. This breakthrough has far-reaching implications for the future of literary translation. It suggests that AI can learn to appreciate the artistry and complexity of language, leading to a richer understanding and appreciation of literary works across cultures. However, challenges remain. Access to high-quality, human-translated texts is crucial, and more research is needed to extend these findings to low-resource languages, ensuring that no language is left behind in this literary renaissance.