However, due to limited amount of training data, the Input layer Feature layer w S Figure 5: Base parser tagging accuracy is not satisfactory even with a larger dropout rate to avoid over-fitting.
the neural stacking structure on top of the English base model trained on UD-Eng achieves a POS tagging accuracy of 89.50% 16 , which corresponds to a 51.50% relative error reduction over the baseline Singlish model, as shown in Table 2.