We propose a neural machine translation architecture that models the
surrounding text in addition to the source sentence. These models lead to
better performance, both in terms of general translation quality and pronoun
prediction, when trained on small corpora, although this improvement largely
disappears when trained with a larger corpus. We also discover that
attention-based neural machine translation is well suited for pronoun
prediction and compares favorably with other approaches that were specifically
designed for this task.

本文提出了一种神经机器翻译架构，该架构不仅针对源语句进行建模，还考虑到上下文信息，这种方法尤其适用于小型语料库的翻译任务，不仅改进了常规翻译质量，还提出了适用于代词预测的基于注意力的神经机器翻译方法，与其他针对该任务进行设计的方法相比具有更好的表现。

神经机器翻译是否从更大的上下文中受益？

Does Neural Machine Translation Benefit from Larger Context?

In this paper, we present our first attempts in building a multilingual
Neural Machine Translation framework under a unified approach. We are then able
to employ attention-based NMT for many-to-many multilingual translation tasks.
Our approach does not require any special treatment on the network architecture
and it allows us to learn minimal number of free parameters in a standard way
of training. Our approach has shown its effectiveness in an under-resourced
translation scenario with considerable improvements up to 2.6 BLEU points. In
addition, the approach has achieved interesting and promising results when
applied in the translation task that there is no direct parallel corpus between
source and target languages.

本研究介绍了一种使用统一方法构建多语言神经机器翻译框架的方法，不需要对网络架构进行特殊处理，能在标准训练方式下学习最少数量的自由参数，在资源匮乏的翻译场景中表现出显著的效果，同时在源语言和目标语言之间没有直接平行语料库的翻译任务中也具有有趣和有前途的结果。