However, since we use a bi-LSTM, the model essentially has two RNNs, and accordingly we have two context vectors, and associated attentions.
context f (i, t) = lstm in(t i−1 ) for the forward RNN and context b (i, t) = lstm in(t i+1 ) for the backward RNN.