Log In Sign Up

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

by   Ning Ding, et al.

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.


page 1

page 2

page 3

page 4


Improving Cross-Domain Chinese Word Segmentation with Word Embeddings

Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite...

MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset

During the past decade, neural network models have made tremendous progr...

Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner

Impressive image captioning results are achieved in domains with plenty ...

Domain adaptation techniques for improved cross-domain study of galaxy mergers

In astronomy, neural networks are often trained on simulated data with t...

Adversarial Bi-Regressor Network for Domain Adaptive Regression

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled ...

Generalization Bounds for Unsupervised Cross-Domain Mapping with WGANs

The recent empirical success of cross-domain mapping algorithms, between...

Fast Cross-domain Data Augmentation through Neural Sentence Editing

Data augmentation promises to alleviate data scarcity. This is most impo...