Structured Document Translation via Format Reinforcement 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 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.
6938d5eddfc35938ba129f1a
Haiyue Song
6938d5eddfc35938ba129f1b
Johannes Eschbach-Dymanus
6938d5eddfc35938ba129f1c
Hour Kaing
6938d5eddfc35938ba129f1d
Sumire Honda
6938d5eddfc35938ba129f1e
Hideki Tanaka
6938d5eddfc35938ba129f1f
Bianka Buschbeck
6938d5eddfc35938ba129f20
Masao Utiyama
Structured Document Translation via Format Reinforcement 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 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 Reinforcement Learning enhances structured text translation by optimizing structure-aware rewards and distinguishing between minor errors and major structural failures.
Format Reinforcement Learning FormatRL Group Relative Policy Optimization TreeSim Node-chrF StrucAUC structured text translation XML HTML document-level translation reward functions