Abstract
The advent of neural machine translation (NMT) has revolutionized cross-lingual communication, yet preserving stylistic nuances remains a significant challenge. While existing approaches often require parallel corpora for style preservation, we introduce Babel, a novel framework that enhances stylistic fidelity in NMT using only monolingual corpora. Babel employs two key components: (1) a style detector based on contextual embeddings that identifies stylistic disparities between source and target texts, and (2) a diffusion-based style applicator that rectifies stylistic inconsistencies while maintaining semantic integrity. Our framework integrates with existing NMT systems as a post-processing module, enabling style-aware translation without requiring architectural modifications or parallel stylistic data. Extensive experiments on five diverse domains (law, literature, scientific writing, medicine, and educational content) demonstrate Babel's effectiveness: it identifies stylistic inconsistencies with 88.21% precision and improves stylistic preservation by 150% while maintaining a high semantic similarity score of 0.92. Human evaluation confirms that translations refined by Babel better preserve source text style while maintaining fluency and adequacy.
Abstract (translated)
神经机器翻译（NMT）的出现彻底改变了跨语言交流，然而保留风格细节仍然是一项重大挑战。现有的方法通常需要平行语料库来保存文本风格，而我们引入了一个名为Babel的新框架，它仅使用单语种语料库就能增强NMT中的风格保真度。Babel采用了两个关键组件：（1）一种基于上下文嵌入的风格检测器，用于识别源文本和目标文本之间的风格差异；以及（2）一种扩散型风格应用器，能够在保持语义完整性的前提下修正风格不一致的问题。我们的框架作为一个后处理模块与现有的NMT系统集成在一起，使得无需架构修改或平行风格数据的情况下就能实现基于风格的翻译。在五个不同领域（法律、文学、科学写作、医学和教育内容）进行的广泛实验表明Babel的有效性：它能够以88.21%的准确度识别风格差异，并且将风格保真度提高了150%，同时保持了语义相似性的高分值为0.92。人类评估确认，经过Babel改进的翻译更好地保留了源文本的风格，同时还保持了流畅性和充分性。
URL
https://arxiv.org/abs/2507.13395
