Neural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information. This may make the translation of a sentence ambiguous or even inconsistent with the translations of neighboring sentences. In order to handle this issue, we propose an inter-sentence gate model that uses the same encoder to encode two adjacent sentences and controls the amount of information flowing from the preceding sentence to the translation of the current sentence with an inter-sentence gate. In this way, our proposed model can capture the connection between sentences and fuse recency from neighboring sentences into neural machine translation. On several NIST Chinese-English translation tasks, our experiments demonstrate that the proposed inter-sentence gate model achieves substantial improvements over the baseline.

通过提出在神经机器翻译中使用相同编码器对两个相连句子进行编码并使用一个句间门控制从前面的句子到当前句子翻译的信息的流量来捕捉句子之间的联系和融合相邻句子的信息，我们的提出的句间门模型在多个NIST中文-英文翻译任务中实现了显着提高。

利用句间门模型将最近性融入神经机器翻译