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Standard neural machine translation (NMT) is on the assumption that thedocument-level context is independent. Most existing document-level NMTapproaches are satisfied with a smattering sense of global document-levelinformation, while this work focuses on exploiting detailed document-levelcontext in terms of a memory network. The capacity of the memory network thatdetecting the most relevant part of the current sentence from memory renders anatural solution to model the rich document-level context. In this work, theproposed document-aware memory network is implemented to enhance theTransformer NMT baseline. Experiments on several tasks show that the proposedmethod significantly improves the NMT performance over strong Transformerbaselines and other related studies.

Document-level Neural Machine Translation with Associated Memory Network