Most successful neural language modeling have mainly focused on English language and operated at the level of words. Although word-level based neural language model performs well, some other languages cannot get word-level token easily especially in Korean language model. In the field of natural language processing for the Korean language, morpheme-level approaches are commonly employed as alternatives to the word in English. However, it causes the dependency to the external morphology analyzer. Accordingly, character-level approaches are preferred for Korean neural language modeling, but there are several ways to represent the Korean language in character-level, as which we call them Korean-letter level and Korean-grapheme level representations. In this thesis, we investigate the best representations of the Korean language with evaluation on three experiments: reconstruction, marker classification, and spelling correction. Since there are no public datasets in the Korean language for the three tasks, we first generate the datasets using a Korean public corpus. Furthermore, we propose an advanced architecture which can effectively employ various representations for sequence-to-sequence problem in the Korean language. In our experiments, we showed that the proposed architecture outperforms the traditional architectures that use only one of the representations.
