# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 4 Experiments and Results
### Main Results

Table 6: Experimental results for seven datasets and three language pairs published by AI Hub. In this paper, such a problem is defined as \"data imbalance\" [9; 59], and the problem must be solved when constructing data in the future. As for high-quality data, it is important that various elements are ultimately built in a balanced manner, and we conducted further analysis in this respect. **Correlation Analysis between LIWC and BLEU score**. We analyzed a correlation between LIWC features and BLEU score for observing the connection between them. We employed BLEU score derived from the Korean-English corpus results in Table 6 and used only LIWC features of the Korean-English case to do this. As shown in Figure 8, we calculated a Pearson correlation [60] joining all the features coming from LIWC, BLEU score (KR-EN), and BLUE (EN-KR). There are numerous correlations of each pair of linguistic features such as powerful negative correlation, which appears between positive emotion and negative emotion and represents as blue. We can infer following result with Figure 8. First, the overall tendency of correlation within LIWC features is mostly not different from analysis in Section 3. For example, Analytic and sentimental levels show a negative correlation. This is a unified result since Analytic indicates whether emotions are excluded and tone of the text is logical. In other words, the results give validity to the LIWC analysis. Secondly, we show that correlations between LIWC features and BLEU score is highly negative. It can be said that training data is good when it has balance in tone, length, gender and so on. However, there are many things to improve in AI Hub such as word  
Figure 8: Results of the correlation between LIWC features and BLEU score (KR-EN). The blue-colored indicates a positive correlation while red-colored indicates a negative correlation. count, punctuation usage, sentimental analysis. It is because there are numerous negative effects in terms of data imbalance. This suggests in which direction we should build data and informs us that the performances can be improved through data cleaning such as PCF [61; 62]. Additionally, we distilled the features by the case of statistically significant negative correlation in Figure 9. It shows the correlation between the BLEU score of MT and linguistic characteristics. This indicates that a negative correlation of them can be created just by changing the source and target language and English-Korean translation has a negative correlation with BLEU score than reverse translation. That is, _English-Korean translation affects the negative effect on BLEU score by those features._ We can infer that many features need to filter than Korean-English translation to compensate its correlation with performance. This result suggests further research on which factors are considered to remove during the data filtering process. In addition, our findings are supported by BLEU score about showing the lower score in EN-KR than KR-EN in terms of data imbalance as shown in Table 6.  
Finally, this paper figured out the association between LIWC and BLEU score in terms of data filtering. It may assist to make guidelines for building datasets later.