让机器翻译文档时保持原有结构，效果显著提升。
Structured Document Translation via Format Reinforcement Learning
- 用强化学习直接优化结构相似性和节点级翻译质量
- 在SAP文档数据集上六项指标均提升，结构错误减少
- 适合需要精准保留文档格式的工业级翻译场景
现有结构化文本翻译多局限于句子级别，难以处理复杂的文档级XML或HTML结构。为此，我们提出格式强化学习（FormatRL），在监督微调模型基础上，采用组相对策略优化方法，直接优化两类新型结构感知奖励：1）TreeSim，衡量预测与参考XML树之间的结构相似性；2）Node-chrF，评估XML节点级别的翻译质量。此外，引入细粒度指标StrucAUC，区分轻微错误与重大结构失败。在SAP软件文档基准上的实验表明，该方法在六项指标上均有提升，分析还揭示了不同奖励函数对结构与翻译质量改进的贡献。
原文摘要 · Abstract (English)
Recent works on structured text translation remain limited to the sentence level, as they struggle to effectively handle the complex document-level XML or HTML structures. To address this, we propose \textbf{Format Reinforcement Learning (FormatRL)}, which employs Group Relative Policy Optimization on top of a supervised fine-tuning model to directly optimize novel structure-aware rewards: 1) TreeSim, which measures structural similarity between predicted and reference XML trees and 2) Node-chrF, which measures translation quality at the level of XML nodes. Additionally, we apply StrucAUC, a fine-grained metric distinguishing between minor errors and major structural failures. Experiments on the SAP software-documentation benchmark demonstrate improvements across six metrics and an analysis further shows how different reward functions contribute to improvements in both structural and translation quality.
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