 and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
<coherence>  and is pre-trained on a joint multi-criteria corpus with meta learning algorithm  . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between the pre-training tasks and the downstream Chinese Word Segmentation tasks, and achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .
 and is pre-trained on a joint multi-criteria corpus with meta learning algorithm . Empirical results show that our PTM-CWS model can utilize the existing prior segmentation knowledge , reduce the discrepancy between pre-trained models and downstream CWS tasks. Besides, METASEG can achieve new state-of-the-art performance on twelve Chinese Word Segmentation corpora .