Abstract：For sentence-level neural machine translation, the problem of incomplete semantic representation is noticeable since the context information of the current sentence is not considered. We extract effective information from each sentence in a document by dependency parsing, and then complement the extracted information into the source sentences, making the semantic representation of the sentences more complete. We conduct experiments on Chinese-English language pair, and propose a training method on large-scale parallel language pairs for the scarcity of document-level parallel corpus. Compared with the baseline model, our approach improves 1.47 BLEU significantly. Experiments show that the document-level neural machine translation based on context recovery can effectively solve the problem of incomplete semantic representation of sentence-level neural machine translation.
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