# Translation via Annotation: A Computational Study of Translating Classical Chinese into Japanese

arXiv: https://arxiv.org/abs/2511.05239 · HTML: https://mlsanity.com/paper/2511.05239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://mlsanity.com/paper/1611.01587/) (2016)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders](https://mlsanity.com/paper/2109.06939/) (2021)
- [WYWEB: A NLP Evaluation Benchmark For Classical Chinese](https://mlsanity.com/paper/2305.14150/) (2023)
- [Time-Aware Ancient Chinese Text Translation and Inference](https://mlsanity.com/paper/2107.03179/) (2021)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2005.03361/) (2020)
- [Kanbun-LM: Reading and Translating Classical Chinese in Japanese Methods by Language Models](https://mlsanity.com/paper/2305.12759/) (2023)

## Abstract (arXiv)

Ancient people translated classical Chinese into Japanese using a system of annotations placed around characters. We abstract this process as sequence tagging tasks and fit them into modern language technologies. The research on this annotation and translation system faces a low resource problem. We alleviate this problem by introducing an LLM-based annotation pipeline and constructing a new dataset from digitized open-source translation data. We show that in the low-resource setting, introducing auxiliary Chinese NLP tasks enhances the training of sequence tagging tasks. We also evaluate the performance of Large Language Models (LLMs) on this task. While they achieve high scores on direct machine translation, our method could serve as a supplement to LLMs to improve the quality of character's annotation.