## 5 Evaluation and Translation Results
### Experiment Results and Discussion  
Table 3 shows the information of the ASPEC-JC corpus. We randomly extracted 300k sentence pairs from 672k training data of the ASPEC-JC corpus for experiments as the training data. Figure 5 shows the changes of BLEU scores on the same test data by epochs with 300k original training data. Figure 6 shows the changes of TER scores on the same test data by epochs with 300k original training data. Figure 7 shows changes of the validation perplexity values on the same development data by epochs with 300k original training data. The proposed methods obtained BLEU and TER scores better than the other methods on the test data in both cases. The best (lowest) perplexities on the development (dev) data after they stopped declining and the BLUE and TER scores of the translation results on the same test-data and the same development-test data by each method are presented in Tables 4 and 5. Figure 6: Translation Error Rate (TER) scores on the test data of 300k sentences. “P1” denotes the Proposed 1 method, “P2” denotes the Proposed 2 method. Figure 7: Validation perplexity values on the development data of 300k sentences. “P1” denotes the Proposed 1 method, “P2” denotes the Proposed 2 method. \"Baseline 1\" was a character-level translation with the 300k original training data. The back-translation models for corpus augmentation were constructed using the 300k original training data of \"Baseline 1\". \"Proposed 1\" was the proposed data augmentation method without consideration of the common Chinese character rates and reuse of the undivided sentences (Section 4.4), which can be used for all language pairs. This method expanded the parallel corpus from the original 300k sentence pairs to 952k sentence pairs in both directions (Japanese\(\rightarrow\)Chinese and Chinese\(\rightarrow\)Japanese). Two hundred and eighteen thousand sentence pairs from 300k training data were used for back-translation in both directions. \"Proposed 2\" was the proposed method, which considered the common Chinese character rates and reused undivided sentences, which can be used only for Chinese-Japanese language pairs. This method expanded the parallel corpus from 300k sentence pairs to 977k and 972k sentence pairs in Japanese\(\rightarrow\)Chinese and Chinese\(\rightarrow\)Japanese directions, respectively. Two hundred twenty seven thousands and 218k sentence pairs from 300k original training data were used for back-translation, in each direction. \"Baseline 2 for P1\" was the back-translation method that back-translated the same data as the Proposed 1 (P1) method did (218k from original training data). The experiment of this method aimed to compare Proposed 1 with the back-translation method (Baseline 2) on the same back-translated data. \"Baseline 2 for P2\" was the back-translation method that back-translated the same data as the Proposed 2 (P2) method did (231k from original training data). The experiment of this method aimed to compare Proposed 2 with the back-translation method (Baseline 2) on the same back-translated data. \"Copied\" was the method that added duplicate copies of the training data as the same times as the Proposed 2 method did. The experiment of this method aimed to highlight differences between the