Multilingual machine translation addresses the task of translating between
multiple source and target languages. We propose task-specific attention
models, a simple but effective technique for improving the quality of
sequence-to-sequence neural multilingual translation. Our approach seeks to
retain as much of the parameter sharing generalization of NMT models as
possible, while still allowing for language-specific specialization of the
attention model to a particular language-pair or task. Our experiments on four
languages of the Europarl corpus show that using a target-specific model of
attention provides consistent gains in translation quality for all possible
translation directions, compared to a model in which all parameters are shared.
We observe improved translation quality even in the (extreme) low-resource
zero-shot translation directions for which the model never saw explicitly
paired parallel data.

我们提出了针对多种源语言和目标语言翻译的任务特定的注意力模型，旨在改进序列到序列神经机器翻译的质量。我们在欧洲议会语料库的四种语言上进行的实验表明，使用目标语言特定的注意力模型相对于参数共享模型提供了一致的翻译质量的提升，甚至在低资源的零 - shot 翻译方向上也观察到了翻译质量的改善。