AI RESEARCH
Source-Grounded Semantic Reinforcement Learning for Low-Resource Target-Language Generation
arXiv CS.AI
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ArXi:2605.29502v1 Announce Type: cross Low-resource target-language generation is often limited by scarce parallel data, while high-resource source-language monolingual data is abundant but difficult to use with standard supervised fine-tuning. We propose Source-Grounded Semantic Reinforcement Learning (SG-SRL), a resource-utilization framework that converts source-language monolingual data into cross-lingual semantic supervision for target-language generation.