用多参考译文提升文学翻译质量，发现高语义相似性数据更有效
Learning from Many Voices: Literary MT Using Multi-Reference Human and Synthetic Data
- 基于语义相似度筛选有差异但忠实的多译文数据
- 中高相似度数据微调效果显著优于低相似度数据
- 人工译文仍优于大模型生成译文，凸显专家价值
单一文学作品存在多种合理译法。本文研究如何利用多参考译文数据提升文学机器翻译性能。提出基于语义相似度的过滤框架，筛选出具有有意义差异但保持忠实性的源文本。实验发现，使用中高语义相似度数据进行微调，显著优于低相似度数据；且其性能可媲美甚至超过使用全部未过滤数据。虽然大语言模型生成的合成译文经济便捷，但在自动指标和人工评估中，基于人工专家译文微调的表现仍优于合成数据，证明了人类专家译文在文学翻译微调中的不可替代性。
原文摘要 · Abstract (English)
Multiple valid translations of a single literary work naturally exist. We investigate strategies for leveraging these multi-reference datasets to improve literary machine translation. We propose a filtering framework based on semantic similarity to identify source texts whose references display meaningful variation while remaining faithful. We find that fine-tuning with medium to high semantic similarity data substantially outperforms low semantic similarity data. Moreover, using medium and high semantic similarity data achieves comparable or better performance than using the full unfiltered data. Synthetic translations generated by LLMs are economical and convenient alternatives to human expert translations; however, we find fine-tuning on human expert translations outperforms fine-tuning on synthetically augmented data in automatic metrics and human evaluations, demonstrating the indispensable value of human expert translations for fine-tuning literary machine translation models.
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