In a March 21, 2024 paper, Fan Zhou and Vincent Vandeghinste from KU Leuven demonstrated that language models can predict the most suitable translation techniques for translation and post-editing tasks. 

The researchers highlighted a set of persistent issues that remain in MT such as word-for-word translation, false friends, ambiguity, information omission or addition, and cultural insensitivity, leading to low-quality translations that may lack clarity and accuracy. These issues arise from the system using incorrect translation techniques, something a translator wouldn’t do. “The human-generated translation process relies on diverse translation techniques, which proves essential to ensuring both linguistic adequacy and fluency,” they emphasized.

Additionally, they highlighted that “utilizing translation techniques is crucial for addressing translation problems, improving translation quality, and ensuring contextually appropriate translations.”

Zhou and Vandeghinste suggested that automatically identifying translation techniques before can effectively guide and improve the machine translation (MT) process. Additionally, these techniques can serve as prompts for large language models (LLMs) to generate high-quality translations.

Source: https://slator.com/

Full article: https://slator.com/language-models-can-predict-the-most-suitable-translation-techniques-study-finds/