融合单词翻译的神经机器翻译

神经机器翻译由于无法完全学习源端单词语义信息,往往造成翻译结果中存在着大量的单词翻译错误。该文提出了一种融入单词翻译用以增强源端信息的神经机器翻译方法。首先使用字典方法找到每个源端单词对应的目标端翻译,然后提出并比较两种不同的方式,用以融合源端单词及其翻译信息: ①Factored 编码器: 单词及其翻译信息直接相加; ②Gated 编码器: 通过门机制控制单词翻译信息的输入。基于目前性能最优的基于自注意力机制的神经机器翻译框架Transformer,在中英翻译任务的实验结果表明,与基准系统相比,该文提出的两种融合源端单词译文的方式均能显著提高翻译性能,BLEU值获得了0.81个点的提升。

Modeling Word Translation to Neural Machine Translation

Due to incapability of fully learning the semantic details of source words, neural machine translation (NMT) tends to have a large number of wrong word translations in translation output. This paper proposes to explicitly incorporate word translation into NMT encoder. Firstly, the dictionary method is used to find the corresponding word translation for each source word. Then two different ways are proposed to fuse the source word and its translation information: (1) Factored Encoder: words and their translation information are added directly; (2) Gated Encoder: controls the input of word translation information through gate mechanism. Based on the state-of-the-art NMT framework of transformer with self-attention mechanism, experimental results on Chinese-English translation task show that the proposed encoders can significantly improve the performance, especially the Gated Encoder method achieves 0.81 BLEU scores improvement over the baseline system.