# Adversarial Multi-Criteria Learning for Chinese Word Segmentation

arXiv: https://arxiv.org/abs/1704.07556 · HTML: https://mlsanity.com/paper/1704.07556/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 7 of 7)

- [Political Depolarization of News Articles Using Attribute-aware Word Embeddings](https://mlsanity.com/paper/2101.01391/) (2021)
- [Unsupervised Word Segmentation with Bi-directional Neural Language Model](https://mlsanity.com/paper/2103.01421/) (2021)
- [That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory](https://mlsanity.com/paper/2209.02967/) (2022)
- [The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS](https://mlsanity.com/paper/2310.08496/) (2023)
- [Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation](https://mlsanity.com/paper/2105.09681/) (2021)
- [Span Labeling Approach for Vietnamese and Chinese Word Segmentation](https://mlsanity.com/paper/2110.00156/) (2021)
- [Chinese Word Segmentation with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2201.08975/) (2022)

## Abstract (arXiv)

Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.