In this work, we adopt a standard LSTM model (Hochreiter and Schmidhuber, 1997) to learn a fixed-size utterance representation v u i ∈ R k , where k is the size of LSTM hidden state.
we first convert the words in each utterance u i to the corresponding word embeddings E u i ∈ R d×|u i | which are then fed into a LSTM for obtaining the last hidden state as the v u i , where d is the dimensionality of word embeddings.