Interactive Machine Translation

Interactive machine translation (IMT) aims to improve the accuracy and efficiency of machine translation by incorporating human feedback during the translation process. Current research focuses on leveraging large language models (LLMs) like mBART and mT5, employing techniques such as prefix-constrained decoding and online learning to refine translations iteratively based on user edits and suggestions. This approach shows promise in reducing post-editing effort and improving translation quality, impacting both computer-aided translation tools and the broader field of human-computer interaction in language processing.