Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only

• URL: http://arxiv.org/abs/2507.13395v1
• Date: Wed, 16 Jul 2025 09:45:11 GMT
• Title: Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only
• Authors: Xuanqi Gao, Weipeng Jiang, Juan Zhai, Shiqing Ma, Siyi Xie, Xinyang Yin, Chao Shen,
• Abstract summary: We introduce Babel, a novel framework that enhances stylistic fidelity in neural machine translation (NMT)<n>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.<n>Our framework integrates with existing NMT systems as a post-processing module, enabling style-aware translation without requiring architectural modifications or parallel stylistic data.
• Score: 24.663850463100346
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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