Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation
arXiv:2606.08011v1 Announce Type: cross Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models. In this work, we propose RLSR (Reinforcement Learning for Source Rewriting), a novel RL-based framework for training a source rewriting model without tuning prompts for each MT model. RLSR optimizes the rewriting model by directly using the improvement in downstream translation quality yielded by each rewr
Rapid advancements in large language models make direct prompting for machine translation enhancement feasible, but the need for automation in this process is becoming critical due to scaling demands.
This development allows machine translation systems to dynamically optimize source rewriting without constant human intervention, significantly improving translation quality and efficiency for various applications.
The reliance on manual prompt engineering for LLM-enhanced machine translation decreases, enabling more adaptive and self-optimizing translation systems with broader applicability.
- · Machine translation providers
- · Businesses with global operations
- · Large Language Model developers
- · NLP researchers
- · Manual prompt engineers
- · Translation services relying solely on traditional rule-based methods
Machine translation output quality improves across diverse language pairs and domains.
Demand for highly customized and effective machine translation solutions increases as integration becomes easier and more robust.
The barrier to entry for highly accurate global communication is lowered, accelerating content localization and cross-cultural information flow.
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Read at arXiv cs.AI