## 5 Evaluation and Translation Results
### Experiment Results and Discussion
Hence, we added the columns \"Raw\" and \"Used\" in the tables to denote the numbers of generated sentences (raw data before removing) and used sentences (after removing unusually long sentences), respectively. The proposed methods obtained BLEU and TER scores better than the baselines did on the development-test data and test data in both cases. Although there were translation errors and unnatural expressions in the generated pseudo-source sentences, the BLEU scores were higher and the TER scores were lower than \"Copied\" and \"Partial\" on the development-test data and test data in both directions, Japanese\(\rightarrow\)Chinese and Chinese\(\rightarrow\)Japanese. The BLEU scores of the \"Partial\" method were lower than the proposed methods, in both directions. Therefore, the mixing step (Section 4.3, Step 2) was necessary. These results demonstrate that the proposed methods were effective for augmenting small-scale parallel corpora to improve translation performance for Japanese\(\rightarrow\)Chinese and Chinese\(\rightarrow\)Japanese NMT. Comparing \"Proposed 1\", \"Proposed 2\" with \"Baseline 2 for P1\", \"Baseline 2 for P2\" in the tables, the results of the proposed methods were nearly identical and better : the proposed method was effective at improving translation accuracy in both directions, Japanese\(\rightarrow\)Chinese and Chinese\(\rightarrow\)Japanese. The experiments described above (Tables 4 and 5) proved the effectiveness of the proposed methods. Nevertheless, our approach was based on only the original parallel data and did not require any additional monolingual data, unlike the back-translation method of [PERSON] et al. [11]. Most methods of corpus augmentation were applied to pair monolingual training data with automatic back-translation and then treat them as additional parallel training data. Therefore, we added comparison experiments. We conducted comparison experiments using 150k and 300k sentences that were randomly extracted from 672k training data of ASPEC-JC as the original data and used the remaining 522k and 372k sentences as the monolingual data. For the comparison experiment, we only implemented our \"Proposed 2\" method because the experiments described above proved that the \"Proposed 2\" method was better than the \"Proposed 1\" method in most cases with the Chinese-Japanese parallel corpus. Translation results obtained on the test data and development-test data are shown in Tables 6-9 with 150k and 300k original training data in both directions, respectively. For Tables 6 and 7, \"Baseline 1\" is a character-level translation, which did not process anything with 150k original training data. The back-translation models were constructed using 150k original training data of Baseline 1 for each method, before corpus augmentation. The back-translation models were constructed using 150k original training data of \"Baseline 1\" for each method, before corpus augmentation. \"Baseline 2 + mono (522k)\" was the back-translation method of [PERSON] et al. (2011), which back-translated the remaining 522k target language sentences of 672k training data to generate 522k pseudo-source sentences directly with no segmentation; the 522k pseudo-source sentences, together with the 522k target sentences, \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multicolumn{6}{c}{**Chinese\(\rightarrow\)Japanese**} \\ \cline{2-9}  & \multicolumn{2}{c}{**\# Sentences**} & \multirow{2}{*}{**\# Back-Translated**} & \multirow{2}{*}{**ppl**} & \multicolumn{2}{c}{**BLEU (\%)**} & \multicolumn{2}{c}{**TER (\%)**} \\ \cline{2-9}  & **Raw** & **Used** & & & **Dev** & **Dev-Test** & **Test** & **Dev-Test** & **Test** \\ \hline Baseline 1 & 150k & 150k & 0 & 4.3 & 36.5 & 36.5 & 48.5 & 50.1 \\ Baseline 2 + mono (522k) & 672k & 672k & 522k & 3.8 & 38.8 & 39.1 & 44.6 & 44.7 \\
150k + mono (522k) + P2 & 2313k & 2201k & 525k & 3.7 & 38.9 & 39.1 & 43.9 & 44.4 \\ \hline \hline \end{tabular}
\end{table}