# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 4 Experiments and Results
### Main Results  
**Performance analysis**. The baseline results of the seven AI Hub parallel corpora are listed in Table 6. The experiment showed a BLEU score of 28.36 for the Korean-English NMT model trained using Korean-English parallel corpora. In the case of the NMT model trained using the Korean-English parallel corpus (technology), the performance showed a BLEU score of over 50, which shows that the words and expressions in a specific domain appear quite repeatedly. For the NMT models based on other fields, the Korean-English parallel corpus (social science) and the Korean-English domain-specialized parallel corpus also demonstrated high performance of 45.64 and 51.88, respectively, in similar contexts. Considering the significant performance gap between domain and general corpora, we can point out probable limitation of corpus construction. Although the performance of all domain corpora is overwhelmingly higher than general corpora, this result does not guarantee that the NMT model is well operating because we randomly extract test set within the training set. Corpora built on the basis of a particular domain typically have significant overlap parts with other sentences within such corpora, but there still exist many different expressions and words present in the field. This can cause difficulties in translating other various expressions. Therefore, our experimental results show that corpus generators should include much more diverse expressions especially in specific domains given that a well-constructed corpora makes a model smarter. **Language direction analysis**. As a result of conducting both Korean to English translation and the opposite based on four Korean-English parallel corpora, the gap of the experimental results for translating Korean to English compared to those of the opposite case differ significantly, with scores from 14.83 to 29.89. This can be interpreted in terms of data construction. Using parallel corpora built by translating sentences from one language to another, translation results can be awkward when training the model in the opposite direction. Thus, a reasonable construction process for training direction-robust NMT models involves building a parallel corpus by constructing about half as the source language and the other half as the target language and translating each. In other words, given the significant differences in performance when changing the direction of translation, it is highly likely that the translation was carried out using only a monolingual corpus, which consists of a source language without considering the opposite direction. Similarly, in the case of Korean-Japanese and Korean-Chinese models, the performance in the opposite direction was significantly reduced. These aspects should be considered when building parallel datasets in the future. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Corpus** & **Language** & **BLEU** \\ \hline Korean–English Parallel corpus & \begin{tabular}{c} KR-EN \\ EN-KR \\ \end{tabular} & \begin{tabular}{c} 28.36 \\ 13.53 \\ \end{tabular} \\ \hline Korean–English Parallel corpus (Social Science) & \begin{tabular}{c} KR-EN \\ EN-KR \\ \end{tabular} & \begin{tabular}{c} 45.64 \\ 17.71 \\ \end{tabular} \\ \hline Korean–English Parallel corpus (Technology) & \begin{tabular}{c} KR-EN \\ EN-KR \\ \end{tabular} & \begin{tabular}{c} 63.88 \\ 39.17 \\ \end{tabular} \\ \hline Korean–English Domain-specialized Parallel corpus & \begin{tabular}{c} KR-EN \\ EN-KR \\ \end{tabular} & \begin{tabular}{c} 51.88 \\ 21.99 \\ \end{tabular} \\ \hline Korean–Japanese Parallel corpus & \begin{tabular}{c} KR-JA \\ JP-KR \\ \end{tabular} & \begin{tabular}{c} 68.88 \\ 49.05 \\ \end{tabular} \\ \hline Korean–Chinese Parallel corpus (Social Science) & \begin{tabular}{c} KR-ZH \\ ZH-KR \\ \end{tabular} & \begin{tabular}{c} 48.74 \\ 25.16 \\ \end{tabular} \\ \hline Korean–Chinese Parallel corpus (Technology) & \
\begin{tabular}{c} KR-ZH \\ ZH-KR \\ \end{tabular} &
\begin{tabular}{c} 46.70 \\ 25.75 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}