Unified Multi-Criteria Chinese Word Segmentation with BERT
Table 2: F1 values on test sets of eight standard CWS datasets. There are four blocks, indicating single criterion methods, multi-task learning framework methods, unified framework methods, and our methods, respectively. The maximum F1 values are highlighted for each dataset.
['Method', 'MSRA', 'AS', 'PKU', 'CTB', 'CKIP', 'CITYU', 'NCC', 'SXU', 'Avg.']
The results are displayed in four blocks. BERT based multi-task method Multi-task BERT and unified method Transformer. These promotions should be attributed to the prior knowledge introduced by BERT and the shared knowledge captured by the unified framework. Besides, we could see that the complete Unified BERT model outperforms the model without bigram features (- Bigram) and the model without auxiliary criterion classification (- CLS), which shows the benefit of bigram features and criterion classification. The complete model obtains the best average performance and achieve new state-of-the-art results for MCCWS, showing the effectiveness of our method.