Title: BED: Boundary-Enhanced Decoder for Chinese Word Segmentation. Abstract: Chinese Word Segmentation (CWS) is an essential fundamental step in the Chinese NLP processing pipeline. In recent years, with the development of deep learning and pre-training language models, many CWS models based on pre-training models, e.g., BERT and Roberta, have been proposed, and the performance of CWS models has been dramatically improved. However, CWS remains an open problem that deserves further study, such as the poor effect on OOV words. To our knowledge, the current proposed CWS approaches mainly focus on optimizing the encoder part of the model, such as incorporating more word information into the encoder or doing pre-training related to the CWS task, etc. And there is no attempt to improve the decoder's performance in the CWS model. This paper proposes an optimized decoder for the CWS model called Boundary-Enhanced Decoder (BED). It could bring 0.05% and 0.69% improvement on Average-F1 and OOV Average-F1 on four benchmark datasets when using a model with a BERT encoder and softmax standard decoder. We also publish our implementation of BED. 