# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 2 Related Works and Background  
### Machine Translation  
Machine Translation refers to a computer system that translates source sentences into target sentences and has achieved significant performance improvements with the advent of deep learning. In 1951, [PERSON] first started research on MT at MIT [25], and it has gradually been developed in the order of rule-based, statistical-based, and deep learning-based MT. **Rule-based Machine Translation**. Rule-based MT (RBMT) [26; 27] is a translation method based on linguistics rules established by linguists, as well as traditional natural language processing such as lexical analysis, syntax analysis, and semantic analysis. For example, the Korean-English RBMT is a methodology that transfers Korean sentence in accordance with the English grammatical rules based on a process of morphological analysis and synthetic analysis, and translates Korean sentence as the source language into English sentence as the target language. This method has the advantage of conducting ideal translation of sentences that conform to the rules, but has the disadvantage of difficulty in extracting grammatical rules and requiring a lot of linguistic knowledge. It is also difficult to expand the translatable language pairs and numerous rules should be considered. **Statistical Machine Translation**. Statistical MT (SMT) [28; 29] is a method of translating using statistical prior knowledge learned from large scale parallel corpus. This method utilizes the alignment and co-occurrence based on statistical information between words from large scale parallel corpus. SMT contains a translation, reordering and language model. It extracts the alignment of the source sentence and target sentence through the translation model, and predicts the probability of the target sentence through the language model. Unlike RBMT, this methodology can be developed without linguistic knowledge and generally higher performance can be obtained by increasing the amount of data. However, building large amounts of data is a challenging task and the context is difficult to understand, because the translation is carried out in words or phrases basis. In the case of SMT, the methodology has changed according to the unit of translation. At the beginning of the study, translation was performed in words. However, in 2003, a translation method of multiple word bundles (i.e., phrase units), was proposed and showed better performance better than word units. The introduction of the concept of variables within the phrase is referred to \"Hierarchical Phrase-Based SMT\", which does not indicate a specific word, such as \"eat an bread\", but rather expressing with the variable X as \"eat X\". The superiority of this approach is that variable X can accommodate a variety of substitute words such as apple and pineapple. Preordered-based SMT is a word order change before translation. In the case of Korean, the word order of the sentence is Subject-Object-Verb (SOV), while English is Subject-Verb-Object (SVO). If the word order is different, this is a methodology to alter the word order in accordance with the word order of the target language to be translated before proceeding with the translation. Syntax Base SMT is a translation technique that changes from \"eat X\" to \"eat NP (Noun Phrase)\" in the Hierarchical Phase-Based SMT. In other words, not all phrases can come to the candidate group, but only nouns can be placed to the candidate group, and it eliminates unnecessary translation candidates in advance [28; 29]. **Neural Machine Translation**. NMT uses deep neural network to translation system. Based on the Sequence to Sequence model, the source language is vectorized through encoder and the latent vector is untangled through decoder to generate the target language. It is a method of utilizing deep neural network to uncover the most appropriate representations and translation results with a single pair of statements of input and output.