Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning
Table 2: Test set OOV rate(%), OOV recall(%) achieved with multi-criteria learning, single-criteria learning.
['[EMPTY]', 'PKU', 'MSR', 'AS', 'CITYU', 'CTB6', 'SXU', 'UD', 'CNC', 'WTB', 'ZX']
From a certain point of view, datasets are complementary to each other since OOV in a dataset may appear in other datasets. To utilize knowledge from each other to improve the OOV recall, our model performs multi-criteria learning with the domain projection layer. To evaluate this, we train the proposed model respectively on each dataset, i.e., single-criteria learning.