Document Level Neural Machine Translation

Document-level neural machine translation (DocNMT) aims to improve machine translation quality by considering the entire document context, rather than translating sentences in isolation. Current research focuses on enhancing model architectures, such as Transformers, to efficiently handle longer sequences and incorporate diverse contextual information, including techniques like improved attention mechanisms and data augmentation strategies to address data sparsity and length bias. DocNMT's significance lies in its potential to generate more coherent and fluent translations, particularly for complex documents, impacting fields like multilingual information retrieval and cross-lingual communication.