Robust Chinese Word Segmentation with Contextualized Word Representations
Table 4: Testing set OOV rate, together with the accuracy achieved on OOV set by our baseline model and our main model, respectively.
['[EMPTY]', 'AS', 'CITYU', 'MSR', 'PKU', 'UD']
We can see that even though the baseline model can have a good accuracy nearly 97% on the whole testing sets of these datasets, the accuracy computed only on OOV words is still stuck in nearly 91%. However, our main model still maintains relatively good performance on OOV words outperforming the baseline model with at most about 5% accuracy, showing the robustness of our model.