Back Translation

Back translation, the process of translating text into a different language and then back to the original, is a data augmentation technique increasingly used to improve machine translation (MT) models, particularly for low-resource languages. Current research focuses on optimizing back-translation methods within various neural architectures like Transformers, exploring techniques such as multilingual transfer learning, and evaluating the impact of different back-translation strategies on model performance and robustness against adversarial attacks. This technique's significance lies in its ability to enhance MT capabilities for under-resourced languages and improve the reliability of AI-generated text detection systems, ultimately impacting both cross-cultural research and the development of more robust AI applications.