Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911063140728832 |
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| author | Gao, Xuanqi Jiang, Weipeng Zhai, Juan Ma, Shiqing Xie, Siyi Yin, Xinyang Shen, Chao |
| author_facet | Gao, Xuanqi Jiang, Weipeng Zhai, Juan Ma, Shiqing Xie, Siyi Yin, Xinyang Shen, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only Gao, Xuanqi Jiang, Weipeng Zhai, Juan Ma, Shiqing Xie, Siyi Yin, Xinyang Shen, Chao Computation and Language Artificial Intelligence 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. |
| title | Mitigating Stylistic Biases of Machine Translation Systems via Monolingual Corpora Only |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.13395 |