# Time-Aware Ancient Chinese Text Translation and Inference

arXiv: https://arxiv.org/abs/2107.03179 · HTML: https://mlsanity.com/paper/2107.03179/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018)
- [Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning](https://mlsanity.com/paper/2102.03554/) (2021)
- [Neural Data-to-Text Generation with LM-based Text Augmentation](https://mlsanity.com/paper/2102.03556/) (2021)
- [Semi-supervised Text Style Transfer: Cross Projection in Latent Space](https://mlsanity.com/paper/1909.11493/) (2019)
- [DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool](https://mlsanity.com/paper/2010.04141/) (2020)
- [Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling](https://mlsanity.com/paper/2102.03551/) (2021)
- [Ancient-Modern Chinese Translation with a Large Training Dataset](https://mlsanity.com/paper/1808.03738/) (2018)

## Built on by (most cited first, 11 of 11)

- [WYWEB: A NLP Evaluation Benchmark For Classical Chinese](https://mlsanity.com/paper/2305.14150/) (2023)
- [SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities](https://mlsanity.com/paper/2304.07778/) (2023)
- [Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation](https://mlsanity.com/paper/2308.00240/) (2023)
- [That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory](https://mlsanity.com/paper/2209.02967/) (2022)
- [C3Bench: A Comprehensive Classical Chinese Understanding Benchmark for Large Language Models](https://mlsanity.com/paper/2405.17732/) (2024)
- [TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models](https://mlsanity.com/paper/2407.03937/) (2024)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)
- [RiverEcho: Real-Time Interactive Digital System for Ancient Yellow River Culture](https://mlsanity.com/paper/2506.21865/) (2025)
- [CHisAgent: A Multi-Agent Framework for Event Taxonomy Construction in Ancient Chinese Cultural Systems](https://mlsanity.com/paper/2601.05520/) (2026)
- [Translation via Annotation: A Computational Study of Translating Classical Chinese into Japanese](https://mlsanity.com/paper/2511.05239/) (2025)
- [L"ushi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model](https://mlsanity.com/paper/2610.16201/) (2026)

## Abstract (arXiv)

In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text. To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era. We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information. % As a natural step of generalization, we pivot on the modern Chinese translations to generate multilingual outputs. %We show experimentally the efficacy of our framework in producing quality translation outputs and also validate our framework on a collected task-specific parallel corpus. We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs. We release both the code and the data https://github.com/orina1123/time-aware-ancient-text-translation for future research.