Kanbun- LM : Reading and Translating Classical C hinese in J apanese Methods by Language Models (2023)

Abstract: 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 of 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.

## introduction classical chinese was introduced to japan approximately 2,000 years ago @xcite . then classical chinese began to be adapted to a japanese form in japanese reading and translating methods in the 8th century a.d. @xcite . this form is called kanbun-kundoku. for simplicity, we call it kanbun in this paper. kanbun has influenced many famous japanese literary works, such as manyoshu @xcite and the tale of genji @xcite . to this day, kanbun still occupies 50 points out of 200 in the common test for japanese university admissions, which shows the deep influence of kanbun on japanese culture. although chinese and japanese have many characters in common, reading classical chinese is not easy for japanese people because of the following 1 https://github.com/nlp-waseda/kanbun-lm two reasons. first, chinese (also classical chinese) is in svo (subject-verb-object) word order, which is the same as english. on the other hand, japanese is in sov (subject-object-verb) word order, which leads to difficulties in understanding chinese. second, chinese is an isolating language with little to no morphological variation and a nearly one-to-one ratio of morphemes to words. however, japanese is an agglutinative language that attaches prefixes and suffixes to a word to indicate the grammatical relationship of that word in a sentence. these differences led to the creation of kanbun. to make the text from svo to sov, from isolating to agglutinative, japanese people developed a system of various conventional reading punctuation, diacritical and syntactic markers @xcite . we list the three main types of markers below and show a specific example of kanbun in figure 1 . since the kanbun system is highly sophisticated, we omit to explain all the rules in this paper. there are also other systems for reading classical chinese in other regions like korean peninsula @xcite and khitan, but we focus on the japanese kanbun system in this paper. kaeriten (ja:返り点) marks placed on the left side of characters indicating the characters need to be read in reverse, making the sentence from svo to sov. (e.g., "我有レ兄" (en:i have a brother) should be read as "我兄有", "レ" is the mark) yomigana (ja:読 み 仮 名) hiragana (japanese phonological units) that are placed on the right side of characters, indicating the characters' reading in japanese. (e.g., "不" (en:no) is read as "ず") okurigana (ja:送り仮名) katakana (phonological units, collectively referred to as kana with hiragana) that are placed on the right side of characters for making the sentence from isolating to agglutinative. (e.g., the chinese character "飲" (en:drink) is "飲む" in japanese, which has an extra kana) figure 1 : an example of kanbun. "春眠不覚暁" (en: this morning of spring in bed i'm lying) is original classical chinese. to transform it into kanbun, we first add kaeriten, yomigana, and okurigana to the sentence. two "レ" on the left side are kaeriten, indicating the characters need to be read in reverse. on the right side, there is a yomigana "ず", meaning "不" should be written as "ず". "エ" and "ヲ" are okurigana, making the sentence from isolating to agglutinative. now if we read the sentence following the above rules, the sentence becomes "春眠暁を覚えず" (while adding marks, we use katakana like "エ" and "ヲ", but in a complete sentence, we use hiragana like "え" and "を". they have no difference except for their looks). compared to the vast amount of research and language resources available for classical chinese, there is little research on kanbun, and the language resources for kanbun are highly scarce. for instance, over 48,900 tang poems (poems written in the characteristic style of the tang dynasty) are included in quan tangshi and are all accessible via the internet. however, to our knowledge, only around 500 tang poems adapted to kanbun are accessible. this large gap makes the research on kanbun increasingly difficult. although a lot of data of kanbun exists in ancient books, it is beyond our ability to apply ocr to them and compile the results into clean data. therefore, building a high-performance classical-chinese-to-kanbun translator is the most efficient way to address the lack of kanbun language resources. moreover, understanding the mechanisms of kanbun will also lead to understanding classical japanese literature (such as wakan konkōbun, a mixture of japanese and chinese writing styles), as well as japanese culture and thought. in previous work, yasuoka @xcite ; yasuoka et al. (2022) proposed a series of applications for classical chinese using universal dependencies @xcite . @xcite proposed a method for classical-chinese-to-kanbun machine translation. however, this method is rulebased and less precise, and the author did not make a dataset to conduct a quantitative evaluation. in this work, we construct the first classical-chineseto-kanbun dataset in the world. based on this, we introduce kanbun-lm, where we fine-tune language models for reading and translating classical chinese in japanese methods, trying to fill the resource gap. the main contributions of our work are summarized as follows: ## our dataset and tasks we construct a parallel dataset for classical chinese and kanbun. the dataset consists of original ancient chinese texts, japanese reading orders, and kanbun texts. we show examples in although it is crucial to choose texts that cover as many periods as possible since vocabulary and grammar change with time, it is difficult to construct a comprehensive dataset. to our knowledge, tangshixuan foot_6 (selection of tang poems) is the largest resource containing both original ancient chinese texts and translated kanbun texts. we use this resource to make our dataset. for preprocessing, we extract the japanese reading order from kanbun by a rule-based program. for the special tokens that may not appear in kanbun or appear multiple times, we annotated them manually. we also convert the characters from old character forms to new character forms (kind of like transforming traditional chinese to simplified chinese, but in japanese character forms) using dictionaries to mitigate the out-of-vocabulary problem. tangshixuan contains a total of 465 poems. we split the dataset using group shuffle split to ensure that all sentences in one poem would not be split. based on the dataset, we introduce two tasks, character reordering and machine translation, both of which are significant in kanbun comprehension. for character reordering, the goal is to transform classical chinese texts into japanese reading orders, from svo to sov. japanese reading orders as ## conclusion and future work in this paper, to address the lack of kanbun language resources, we used language models to read classical chinese in japanese reading orders and translate classical chinese into kanbun. we constructed the first classical-chinese-to-kanbun dataset in the world, which includes original ancient chinese texts, translated kanbun texts, and the japanese reading orders. furthermore, we introduced two tasks for the dataset: character reordering and machine translation. we achieved state-of-the-art results in both tasks, which have a great lead over the baseline. we also constructed a pipeline for the two tasks and verified that accurate pre-reordering is helpful for machine translation. however, the accuracy of current reordering models is not enough, and future efforts are needed to improve the accuracy. moreover, we discussed which automatic evaluation metric is the most suitable for classical-chinese-to-kanbun translation by computing the correlation between the automatic and human evaluation metrics. in our experiments, bertscore is the best. however, we only tested with characterbased metrics. more experiments are still needed to test subword-based and sentence-based metrics. in the future, we hope to continuously update the dataset to include an increasingly comprehensive range of ancient texts. we also hope to collaborate with experts in classical chinese to find the upper bound of human character reordering accuracy, refine the manual evaluation metrics to a more streamlined one, and make a deeper exploration on the best automatic evaluation metric.