论文标题
Bi-Decoder增强神经机器翻译网络
Bi-Decoder Augmented Network for Neural Machine Translation
论文作者
论文摘要
近年来,神经机器翻译(NMT)已成为一种流行的技术,编码器框架框架是所有方法中的主流。显然,编码的语义表示质量非常重要,并且可能会显着影响模型的性能。但是,现有的单向源对目标架构几乎不会产生与文本无关的语言表示,因为它们严重依赖于给定语言对的特定关系。为了减轻这个问题,在本文中,我们提出了一个新颖的双学位增强网络(Bidan),以完成神经机器翻译任务。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,以在培训时间生成源语言序列。由于每个解码器都将输入文本的表示形式转换为相应的语言,因此用两个目标端共同训练可以使共享的编码器有可能产生独立于语言的语义空间。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。
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.