仅用单语语料提升机器翻译风格一致性，无需双语数据。
Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only
- 通过上下文嵌入识别源文与译文风格差异，再用扩散模型修正不一致。
- 在五个领域测试中风格保留提升150%，语义相似度达0.92。
- 无需修改模型架构或平行风格数据，适合实际部署的翻译系统。
神经机器翻译（NMT）虽推动了跨语言交流，但保持风格细节仍是挑战。现有方法常需平行语料进行风格保留，本文提出Babel框架，仅使用单语语料提升翻译风格保真度。Babel包含两个核心组件：（1）基于上下文嵌入的风格检测器，用于识别源文与目标文间的风格差异；（2）基于扩散模型的风格应用模块，在保持语义完整性的前提下修正风格不一致。该框架作为后处理模块集成于现有NMT系统，无需架构改动或平行风格数据。在法律、文学、科技、医学及教育内容等五个不同领域开展的实验表明，Babel能以88.21%的精度识别风格差异，风格保留性能提升150%，同时保持0.92的高语义相似度。人工评估证实，经Babel优化后的译文更准确保留原文风格，且流畅性与准确性俱佳。
原文摘要 · Abstract (English)
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.
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