# Ancient-Modern Chinese Translation with a Large Training Dataset

arXiv: https://arxiv.org/abs/1808.03738 · HTML: https://mlsanity.com/paper/1808.03738/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [CCPM: A Chinese Classical Poetry Matching Dataset](https://mlsanity.com/paper/2106.01979/) (2021)
- [Time-Aware Ancient Chinese Text Translation and Inference](https://mlsanity.com/paper/2107.03179/) (2021)
- [Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE](https://mlsanity.com/paper/2310.09550/) (2023)
- [Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation](https://mlsanity.com/paper/2104.05964/) (2021)
- [Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation](https://mlsanity.com/paper/2308.00240/) (2023)
- [WenyanGPT: A Large Language Model for Classical Chinese Tasks](https://mlsanity.com/paper/2504.20609/) (2025)
- [System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5](https://mlsanity.com/paper/2606.12392/) (2026)
- [Efficiently Building a Domain-Specific Large Language Model from Scratch: A Case Study of a Classical Chinese Large Language Model](https://mlsanity.com/paper/2505.11810/) (2025)
- [Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors](https://mlsanity.com/paper/2601.05508/) (2026)

## Abstract (arXiv)

Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatic translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quintessence of the ancients. However, the lack of large-scale parallel corpus limits the study of machine translation in Ancient-Modern Chinese. In this paper, we propose an Ancient-Modern Chinese clause alignment approach based on the characteristics of these two languages. This method combines both lexical-based information and statistical-based information, which achieves 94.2 F1-score on our manual annotation Test set. We use this method to create a new large-scale Ancient-Modern Chinese parallel corpus which contains 1.24M bilingual pairs. To our best knowledge, this is the first large high-quality Ancient-Modern Chinese dataset. Furthermore, we analyzed and compared the performance of the SMT and various NMT models on this dataset and provided a strong baseline for this task.