Ensembling is a well-known technique in neural machine translation (NMT). Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On Japanese-English we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.

该研究通过将集合模型解开成单个大型神经网络，并采用降维技术，旨在提高神经机器翻译的性能和运行速度。在英日翻译任务中，该网络的大小和解码速度与单个 NMT 网络相当，而性能却相当于一个 3-ensemble 系统。

神经机器翻译集成的展开和收缩