Reading and Translating Classical Chinese in Japanese Methods by Language Models

Abstract

Recent studies in natural language processing (NLP) have focused on modern languages and achieved state-of-the-art results in several 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 that have significantly impacted Japanese literature. However, compared with the abundant resources for ancient texts in mainland China, Kanbun resources remain scarce in Japan. Therefore, we construct a parallel Classical-Chinese-to-Kanbun dataset consisting of the Tang poetry. 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: https://github.com/nlp-waseda/Kanbun-LM.