Backtranslation Augmented Direct Preference Optimization for Neural Machine Translation
Contemporary neural machine translation (NMT) systems are almost exclusively built by training on supervised parallel data. Despite the tremendous progress achieved, these systems still exhibit persistent translation errors. This paper proposes that a post-training paradigm based on reinforcement learning (RL) can effectively rectify such mistakes. We introduce a novel framework that requires only a general text corpus and an expert translator which can be either human or an AI system to provide iterative feedback. In our experiments, we focus specifically on English-to-German translation as a representative high-resource language pair. Crucially, we implement this RL-based post-training ...
| Authors | Mehrdad Ghassabi, Spehr Rajabi, Hamidreza Baradaran Kashani et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 April 2026 |
| Last updated at source | 2 August 2026 |
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