The field of natural language processing and machine translation is rapidly evolving, with a focus on developing more sophisticated and culturally sensitive models. Researchers are exploring the use of AI to express emotion and convey cultural nuances, such as in the visualization of emotions in poetry through Chinese calligraphy. Another area of focus is the development of more accurate and efficient evaluation metrics for document-level translation, including the use of large language models and machine learning algorithms. Additionally, there is a growing interest in applying these technologies to specialized domains, such as medical translation, and to under-resourced languages, like Kurdish. Noteworthy papers in this area include: PoEmotion, which combines natural language processing and deep learning generative algorithms to create Chinese calligraphy that conveys emotions in poetry. The study on Testing LLMs' Capabilities in Annotating Translations, which investigates the potential of large language models to identify and categorize errors in specialized translations.