We then feed the embedded sequence w = {w 1 , w 2 , ..., w T } into a bidirectional LSTM (Graves and Schmidhuber, 2005).
each input x t is associated with a contextualized feature vector where − → f t and ← − f t represent the left and right context at time step t of the sequence.