For each element h n in bi-RNNs outputs, we compute a scalar selfattention score as follows: The final utterance representation E(x) is the weighted sum of bi-RNNs outputs: (3) After getting the encoding of each sentence in C t , we input these sentence embeddings to another GRU-based RNNs to obtain the context embedding E(C t ) as follows: In the existing work (Williams et al., 2017;Bordes et al., 2016;Li et al., 2017), after getting the context representation, the dialogue system will give a response y t to the user based on p(y t |C t ).
the dialogue system may give unreasonable responses if unexpected queries happen.