Structured Document Translation via Format Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915654327599104 |
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| author | Song, Haiyue Eschbach-Dymanus, Johannes Kaing, Hour Honda, Sumire Tanaka, Hideki Buschbeck, Bianka Utiyama, Masao |
| author_facet | Song, Haiyue Eschbach-Dymanus, Johannes Kaing, Hour Honda, Sumire Tanaka, Hideki Buschbeck, Bianka Utiyama, Masao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_05100 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structured Document Translation via Format Reinforcement Learning Song, Haiyue Eschbach-Dymanus, Johannes Kaing, Hour Honda, Sumire Tanaka, Hideki Buschbeck, Bianka Utiyama, Masao Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Structured Document Translation via Format Reinforcement Learning |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.05100 |