  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.
