Empower Sequence Labeling with Task-Aware Neural Language Model
Table 9: Effect of hidden state size of LSTM
['Model', 'State Size', 'F1score±std', 'Recall±std', 'Precision±std']
To explore the effect of model size, we train our model with different hidden state sizes. For comparison, we also apply the same hidden state sizes to LSTM-CRF and LSTM-CNN-CRF. LSTM-CRF has a drop at state size 200. This phenomenon further verified our intuition of employing the language model to extract knowledge and prevent overfitting.