# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 5 Discussion and Positive Impact of This Study  
This paper conducted in-depth analyses on various parallel corpora published by AI Hub. Structural components that directly determine the quality of each corpus were closely investigated through the LIWC, and the actual usability of each corpus was quantitatively evaluated through the NMT model trained by the corresponding corpus. Through these, we have posed a positive impact on the machine translation research fields and figured out the desirable direction of data construction. Specifically, main contributions of our paper can be described as follows:  
First, to the best of our knowledge, for the first time, we performed quantitative and qualitative in-depth analyses on AI Hub data. We adopted LIWC as an investigation tool for parallel corpus, and derived various meaningful information (e.g., _quality of parallel corpus_) by newly interpreting each component obtained from LIWC. As LIWC was generally used in psychological research, various aspects of corpus analysis were possible, such as morphological analysis, Syntactic analysis, and so on. It can be confirmed that the results were suitable for the features of each corpus in most cases. For example, in Section 3.2, informal language markers such as swear word and filler rather used although they rarely used in other corpus. It is because the corpus includes dialogues and spoken words. Additionally, the result of Word Count and Commas in Section 3.3 showed that Domain-Specialized Parallel Corpus tends to explain terminology in long sentences with commas. In Section 3.4, the Emotional tone of the technology corpus was relatively low in order to concisely explicate the terminology, not a description of emotions. Since there are many texts with the economy as a topic in Section 3.5, money of personal interest topic has the highest rate than other corpora. Furthermore, word per sentence in Sections 3.6 and 3.7  
Figure 9: Negative correlation (\(r<0\)) results of the important factors between BLEU score (KR-EN) and LIWC features. The empty cells (white) indicate cut-offed due to the positive value. Note that this result is statistically significant as \(p<0.05\). was the longest due to the characteristics of Chinese, which rarely uses spaces. Away from model-centric machine translation studies, this paper encourages data-centric research differently. This can have a positive impact on the NMT research field by presenting a new perspective. Second, we pointed out the problems of the data construction process by revealing that there was a significant discrepancy in performance between English-Korean and Korean-English NMT model trained by the identical parallel corpus. It can be inferred that it is caused by the improper construction strategy. When constructing a parallel corpus that comprises certain two languages (i.e., _Korean and English_), it is desirable to construct a balanced corpus by translating a half of the translation into the first language based on the second language and the remaining half of the translation into the second language based on the first language. Through the empirical analysis, we point out that this aspect may underestimated. For the last, we revealed that several important factors that determine the quality of corpus. In Section 3.5, we can infer that domain uniformity is neglected because it contains medical text in social science corpus. The gender bias, which had a major influence on the quality of corpus, was also overlooked in several corpora, especially in Sections 3.3 and 3.5, there was a double gender bias. Additionally, we proposed that subject omission and cross-reference resolution problems should be further considered for ensuring the high quality data. Eventually, this paper clearly analyzed the strengths and weaknesses of the existing AI Hub data and provided insight into the future direction of data construction. In general, in the case of data filtering, mathematical and modeling approaches are taken [61,63].