  Benefiting from the strong ability of the pre-trained model, the research on
Chinese Word Segmentation (CWS) has made great progress in recent years.
However, due to massive computation, large and complex models are incapable of
empowering their ability for industrial use. On the other hand, for
low-resource scenarios, the prevalent decode method, such as Conditional Random
Field (CRF), fails to exploit the full information of the training data. This
work proposes a fast and accurate CWS framework that incorporates a
light-weighted model and an upgraded decode method (PCRF) towards industrially
low-resource CWS scenarios. First, we distill a Transformer-based student model
as an encoder, which not only accelerates the inference speed but also combines
open knowledge and domain-specific knowledge. Second, the perplexity score to
evaluate the language model is fused into the CRF module to better identify the
word boundaries. Experiments show that our work obtains relatively high
performance on multiple datasets with as low as 14\% of time consumption
compared with the original BERT-based model. Moreover, under the low-resource
setting, we get superior results in comparison with the traditional decoding
methods.
