Translation Quality

Evaluating machine translation (MT) quality focuses on assessing the accuracy, fluency, and overall naturalness of translated text, often comparing machine-generated translations to human references or using automatic metrics. Current research emphasizes improving MT quality through techniques like retrieval-augmented generation, preference-based alignment using LLMs (e.g., reinforcement learning from human feedback), and multi-agent collaborative approaches for complex texts. These advancements are crucial for enhancing cross-lingual communication and enabling broader access to information, impacting fields ranging from e-commerce to scientific research.