On the other hand, in the field of word embedding construction, some previous researchers found that character n-grams are more useful than single characters<cite> (Wieting et al. 2016</cite>; Bojanowski et al. 2017) . In particular,<cite> (Wieting et al. 2016)</cite> demonstrated that constructing word embeddings from character n-gram embeddings outperformed the methods that construct word embeddings from character embeddings by using CNN or a Long Short-Term Memory (LSTM). Based on their reports, in this paper, we propose a neural language model that utilizes character n-gram embeddings.