  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.
