Cross-cultural adaptation of English language and literature through context-augmented neural machine translation

The cross-cultural adaptation of the English language and literature plays a crucial role in fostering global communication and understanding. This paper proposes a novel approach to enhance document-level Neural Machine Translation (NMT) models by incorporating a hierarchical global context derived from the entire document. Specifically, the proposed model obtains the dependencies between the word in the current sentence and all sentences and words in the document, respectively, and combines the dependencies at different levels to obtain the global context representation containing hierarchical contextual information. Finally, each word in the current sentence of the source language acquires its unique context that integrates word-and sentence-level dependencies. This paper proposes a two-step training strategy to use the advantages of parallel sentence pairs in training fully. Experiments on several benchmark corpus data sets show that the proposed model achieves significant translation quality improvement compared with several strong baselines.