Machine Translation Evaluation

Machine translation evaluation aims to objectively assess the quality of automatically generated translations, typically by comparing them to human-created references. Current research heavily focuses on leveraging large language models (LLMs) and other deep learning architectures to automate this process, exploring various prompting techniques and addressing challenges in low-resource language settings and reference-free evaluation. These advancements are crucial for improving machine translation systems and enabling more reliable and nuanced assessments of translation quality across diverse languages and domains.