For the middle layer, we use Long Short-Term Memory (LSTM) for each model of biRNN with 200 hidden states, and FFNN with a unit size of 200 and 100 from the near side of the input layer.
by using these approaches, word segmentation is not necessary, and the vocabulary to be handled is reduced; however, there are few studies that use the attention mechanism in character-based approaches.