In addition to the above similarities, words in the current sentence are encoded into the state vector using a Long Short Term Memory (LSTM) network and mean pooling.
we take the sequence of words in the current sentence as input, pass their one-hot vector embeddings to the LSTM cells, and output a corresponding vector representation, which combined with the similarities (described above) produces a state vector to serve as the input for the DQN module.