The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition
Table 4: Performance of our model when using one and two layers
['Entity', 'Bi-LSTM Pre.', 'Bi-LSTM Rec.', 'Bi-LSTM  [ITALIC] F1', 'LSTM Pre.', 'LSTM Rec.', 'LSTM  [ITALIC] F1']
S4SS3SSS0Px2 Effect of Bidirectional Learning In the second experiment, we examine the benefit of accessing both past and future contexts by comparing the performances of RNN, LSTM and Bi-LSTM models. In this task, RNN model fails because it faces the gradient vanishing/exploding problem when training with long-range dependencies (132 time steps), leading to the unstable value of the cost functions.