# That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory

arXiv: https://arxiv.org/abs/2209.02967 · HTML: https://mlsanity.com/paper/2209.02967/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation](https://mlsanity.com/paper/1906.11455/) (2019)
- [Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF](https://mlsanity.com/paper/1704.01314/) (2017)
- [Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning](https://mlsanity.com/paper/1903.04190/) (2019)
- [Time-Aware Ancient Chinese Text Translation and Inference](https://mlsanity.com/paper/2107.03179/) (2021)
- [Neural Word Segmentation with Rich Pretraining](https://mlsanity.com/paper/1704.08960/) (2017)
- [Adversarial Multi-Criteria Learning for Chinese Word Segmentation](https://mlsanity.com/paper/1704.07556/) (2017)
- [Switch-LSTMs for Multi-Criteria Chinese Word Segmentation](https://mlsanity.com/paper/1812.08033/) (2018)
- [Neural Chinese Word Segmentation with Dictionary Knowledge](https://mlsanity.com/paper/1807.05849/) (2018)
- [Improving Cross-Domain Chinese Word Segmentation with Word Embeddings](https://mlsanity.com/paper/1903.01698/) (2019)

## Built on by (most cited first, 5 of 5)

- [AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models](https://mlsanity.com/paper/2403.06574/) (2024)
- [An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling](https://mlsanity.com/paper/2402.13534/) (2024)
- [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)
- [Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA](https://mlsanity.com/paper/2509.01158/) (2025)
- [From Inline Notes to Collected Commentaries: Toward Context-Preserving Organization of Exegetical Knowledge in Classical Chinese Texts](https://mlsanity.com/paper/2607.29044/) (2026)

## Abstract (arXiv)

The evolution of language follows the rule of gradual change. Grammar, vocabulary, and lexical semantic shifts take place over time, resulting in a diachronic linguistic gap. As such, a considerable amount of texts are written in languages of different eras, which creates obstacles for natural language processing tasks, such as word segmentation and machine translation. Although the Chinese language has a long history, previous Chinese natural language processing research has primarily focused on tasks within a specific era. Therefore, we propose a cross-era learning framework for Chinese word segmentation (CWS), CROSSWISE, which uses the Switch-memory (SM) module to incorporate era-specific linguistic knowledge. Experiments on four corpora from different eras show that the performance of each corpus significantly improves. Further analyses also demonstrate that the SM can effectively integrate the knowledge of the eras into the neural network.