  Recent studies have shown effectiveness in using neural networks for Chinese
word segmentation. However, these models rely on large-scale data and are less
effective for low-resource datasets because of insufficient training data. We
propose a transfer learning method to improve low-resource word segmentation by
leveraging high-resource corpora. First, we train a teacher model on
high-resource corpora and then use the learned knowledge to initialize a
student model. Second, a weighted data similarity method is proposed to train
the student model on low-resource data. Experiment results show that our work
significantly improves the performance on low-resource datasets: 2.3% and 1.5%
F-score on PKU and CTB datasets. Furthermore, this paper achieves
state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets.
