基于格式强化学习的结构化文档翻译

Structured Document Translation via Format Reinforcement Learning

December 4, 2025

作者: Haiyue Song, Johannes Eschbach-Dymanus, Hour Kaing, Sumire Honda, Hideki Tanaka, Bianka Buschbeck, Masao Utiyama

cs.AI

摘要

近期关于结构化文本翻译的研究仍局限于句子层面，难以有效处理复杂的文档级XML或HTML结构。为此，我们提出格式强化学习（FormatRL）方法，该方法在监督微调模型基础上采用组相对策略优化，直接优化两种新型结构感知奖励指标：1) TreeSim——衡量预测XML树与参考XML树的结构相似度；2) Node-chrF——在XML节点层面评估翻译质量。此外，我们应用了StrucAUC这一能区分细微错误与重大结构故障的细粒度评估指标。在SAP软件文档基准测试上的实验表明，该方法在六项指标上均有提升，进一步分析揭示了不同奖励函数如何协同提升结构完整性与翻译质量。

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 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.