Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder-decoder framework is the mainstream among all the methods. It's obvious that the quality of the semantic representations from encoding is very crucial and can significantly affect the performance of the model. However, existing unidirectional source-to-target architectures may hardly produce a language-independent representation of the text because they rely heavily on the specific relations of the given language pairs. To alleviate this problem, in this paper, we propose a novel Bi-Decoder Augmented Network (BiDAN) for the neural machine translation task. Besides the original decoder which generates the target language sequence, we add an auxiliary decoder to generate back the source language sequence at the training time. Since each decoder transforms the representations of the input text into its corresponding language, jointly training with two target ends can make the shared encoder has the potential to produce a language-independent semantic space. We conduct extensive experiments on several NMT benchmark datasets and the results demonstrate the effectiveness of our proposed approach.

中文翻译：

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用于神经机器翻译的双解码器增强网络

神经机器翻译（NMT）近年来成为一种流行技术，编码器-解码器框架是所有方法中的主流。很明显，编码语义表示的质量非常重要，并且会显着影响模型的性能。然而，现有的单向源到目标架构可能很难产生与语言无关的文本表示，因为它们严重依赖于给定语言对的特定关系。为了缓解这个问题，在本文中，我们为神经机器翻译任务提出了一种新颖的双解码器增强网络（BiDAN）。除了生成目标语言序列的原始解码器外，我们还添加了一个辅助解码器来在训练时生成回源语言序列。由于每个解码器都将输入文本的表示转换为其对应的语言，因此与两个目标端联合训练可以使共享编码器有可能产生与语言无关的语义空间。我们对几个 NMT 基准数据集进行了广泛的实验，结果证明了我们提出的方法的有效性。