Considering the last few tokens of a sequence normally have more influence on the representation of the whole sequence learnt with LSTM because they get through less times of forget gate from the LSTM computation unit, to effectively capture the information from both beginning and end of an EDU, we use bidirectional LSTM to learn the representation of an EDU.
one LSTM takes the word sequence in forward order as input, the other takes the word sequence in reversed order as input.