论文标题
通用向量神经机器翻译，有效注意
Universal Vector Neural Machine Translation With Effective Attention
论文作者
论文摘要
神经机器翻译（NMT）利用一个或多个训练有素的神经网络来翻译短语。 Sutskever引入了基于序列的编码器模型的序列，该模型成为基于NMT的系统的标准。后来引入了注意机制，以解决长期句子的翻译和提高整体准确性的问题。在本文中，我们提出了一个基于编码器模型的神经机器翻译的单数模型。大多数翻译模型都被训练为一种翻译模型。我们引入了中性/通用模型表示形式，可用于根据源和提供的目标来预测多种语言。其次，我们通过将整体学习向量添加到乘法模型中引入了注意力模型。通过这两个更改，通过使用新颖的通用模型，多种语言翻译应用所需的模型数量减少了。
Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever introduced a sequence to sequence based encoder-decoder model which became the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sentences and improving overall accuracy. In this paper, we propose a singular model for Neural Machine Translation based on encoder-decoder models. Most translation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on the source and a provided target. Secondly, we introduce an attention model by adding an overall learning vector to the multiplicative model. With these two changes, by using the novel universal model the number of models needed for multiple language translation applications are reduced.
