In computer-aided translation tasks, reducing the time of reviewing and post-editing on translations is meaningful for human translators. However, existing studies mainly aim to improve overall translation quality, which only reduces post-editing time. In this work, we firstly identify testing sentences which are highly similar to training set (high-potential sentences) to reduce reviewing time, then we focus on improving corresponding translation quality greatly to reduce post-editing time. From this point, we firstly propose two novel translation memory methods to characterize similarity between sentences on syntactic and template dimensions separately. Based on that, we propose a similarity-aware neural machine translation (similarity-NMT) which consists of two independent modules: (1) Identification Module, which can identify high-potential sentences of testing set according to multi-dimensional similarity information; (2) Translation Module, which can integrate multi-dimensional similarity information of parallel training sentence pairs into an attention-based NMT model by leveraging posterior regularization. Experiments on two Chinese \(\Rightarrow \) English domains have well-validated the effectiveness and universality of the proposed method of reducing human translator efforts.

在计算机辅助翻译任务中，减少人工翻译的审阅和后期编辑时间对于翻译人员而言意义重大。但是，现有研究主要旨在提高整体翻译质量，这只会减少后期编辑时间。在这项工作中，我们首先确定与训练集高度相似的测试句子（高可能句子），以减少审阅时间，然后我们将重点放在大大提高相应的翻译质量上，以减少后期编辑时间。从这一点出发，我们首先提出两种新颖的翻译记忆方法，分别描述句法和模板维度上句子之间的相似性。在此基础上，我们提出了一种基于相似度的神经机器翻译，由两个独立的模块组成：（1）识别模块，可以根据多维相似度信息识别测试集的高位句子；（2）翻译模块，可以利用后验正则化将并行训练句子对的多维相似性信息集成到基于注意力的NMT模型中。在两个中文\（\ Rightarrow \）上进行的实验 英语领域已经很好地验证了所提出的减少人工翻译工作量的方法的有效性和普遍性。