# Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation

arXiv: https://arxiv.org/abs/2007.08186 · HTML: https://mlsanity.com/paper/2007.08186/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval](https://mlsanity.com/paper/2203.03367/) (2022)
- [PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation](https://mlsanity.com/paper/2201.02009/) (2022)
- [Chinese Word Segmentation with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2201.08975/) (2022)
- [Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling](https://mlsanity.com/paper/2210.15231/) (2022)
- [Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training](https://mlsanity.com/paper/2404.05560/) (2024)
- [Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation](https://mlsanity.com/paper/2412.09045/) (2024)

## Abstract (arXiv)

Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS). Nevertheless, the performance of supervised models tends to drop dramatically when they are applied to out-of-domain data. Performance degradation is caused by the distribution gap across domains and the out of vocabulary (OOV) problem. In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS. For distant annotation, we rethink the essence of "Chinese words" and design an automatic distant annotation mechanism that does not need any supervision or pre-defined dictionaries from the target domain. The approach could effectively explore domain-specific words and distantly annotate the raw texts for the target domain. For adversarial training, we develop a sentence-level training procedure to perform noise reduction and maximum utilization of the source domain information. Experiments on multiple real-world datasets across various domains show the superiority and robustness of our model, significantly outperforming previous state-of-the-art cross-domain CWS methods.