This raises the question of whether features extracted with BiLSTMs in themselves capture information about subtrees, thus making recursive composition superfluous.
each time we attach a dependent to a head, we construct a vector which is a concatenation of h, d and r. We pass this vector to the LSTM of h. c is the output state of the LSTM after passing through that vector.