  Recent studies in natural language processing (NLP) have focused on modern
languages and achieved state-of-the-art results in many tasks. Meanwhile,
little attention has been paid to ancient texts and related tasks. Classical
Chinese first came to Japan approximately 2,000 years ago. It was gradually
adapted to a Japanese form called Kanbun-Kundoku (Kanbun) in Japanese reading
and translating methods, which has significantly impacted Japanese literature.
However, compared to the rich resources for ancient texts in mainland China,
Kanbun resources remain scarce in Japan. To solve this problem, we construct
the first Classical-Chinese-to-Kanbun dataset in the world. Furthermore, we
introduce two tasks, character reordering and machine translation, both of
which play a significant role in Kanbun comprehension. We also test the current
language models on these tasks and discuss the best evaluation method by
comparing the results with human scores. We release our code and dataset on
GitHub.
