## 1 Introduction  
In recent years, Neural Machine Translation (NMT) has made remarkable achievements [1]. Actually, NMT has achieved good results with large-scale parallel corpora. However, in low-resource languages or domain-defined translation tasks, the parallel corpus is small. Accordingly, the translation performance is severely constrained [2]. Communication during a crisis must be multilingual, and multilingual crisis communications is enabled through translation; in addition, low-resource languages need to be translated [3]. Therefore, studies of NMT under the condition of a low-resource language corpus have high practical value. [PERSON] et al. [4] used attention mechanisms in several language pairs for NMT and improved the translation performance of other low-resource language pairs by transferring parameters from the used attention mechanisms of the trained NMT models. This method greatly improved the translation performance of low-resource language pairs. However, all experiments were in European language pairs. Language pairs that have markedly different linguistic structures were not included. Zero-shot translation is a translation mechanism that uses a single NMT engine to translate between multiple languages, even such low-resource languages for which no direct parallel data were provided during training. This type of multi-language method for NMT was mainly proposed by Google [5]. [PERSON] et al. [6] proposed a simple iterative training procedure that leverages a duality of translations directly generated by the system for the zero-shot directions. [PERSON] et al. [7] focused on languages with sparse training data. The use of a pivot language for low-resource language pairs can also be effective for NMT. For example, given three languages A, B, and C, if no direct parallel corpora exist between A and C, but parallel corpora between A and B and between B and C do exist, then B is useful as a pivot language to achieve translation from A to C. Based on this work, the \"teacher-student\" framework [8], maximum likelihood estimation method [PERSON] et al. [9], and joint training method [PERSON] et al. [10] have also been proposed. Expanding the size of the training data (parallel corpus) is also an effective way to improve the translation performance for NMT in low-resource language pairs. [PERSON] et al. [11] proposed a method that generates pseudo-parallel data with monolingual data and back-translation. In this paper, we propose a method to augment a parallel corpus by sentence segmentation and synthesis. This method has two variations: one is for all language pairs, and the other is for the Chinese-Japanese language pair. Our method splits long sentence pairs (properly speaking, sentence pairs that contain punctuation marks) in the corpus into parallel partial sentence pairs, back-translates the target partial sentences, then synthesizes pseudo-parallel sentence pairs by combining the source partial sentences and the back-translated target partial sentences to expand the corpus size. The method uses the word alignment information of each parallel sentence pair to produce parallel partial sentence pairs. One variation of the method modifies the alignment information with common Chinese character information of the Chinese and Japanese segments. The Chinese language is written with traditional or simplified Chinese characters. In this paper, we consider only simplified Chinese. In our experiments, we used [PERSON]'s NMT system as the base system [12]. It follows an encoder-decoder architecture with global attention. In our case, we chose character-level NMT as the baseline, also because character-level NMT between Japanese and Chinese has better translation performance than word-level NMT. The main contributions of this paper are the following. We show that we can improve the NMT system's translation performance by mixing generated pseudo-parallel sentence pairs into training data with no monolingual data and without changing the neural network architecture.