  This study aims to compare three methods for translating ancient texts with
sparse corpora: (1) the traditional statistical translation method of phrase
alignment, (2) in-context LLM learning, and (3) proposed inter methodological
approach - statistical machine translation method using sentence piece tokens
derived from unified set of source-target corpus. The performance of the
proposed approach in this study is 36.71 in BLEU score, surpassing the scores
of SOLAR-10.7B context learning and the best existing Seq2Seq model. Further
analysis and discussion are presented.
