Each input is represented as a sequence of words which is encoded to a vector representation via global-local self-attentive encoder (GLE) module (Zhong et al., 2018).
gLE employs local slot-specific bidirectional LSTMs and a global bidirectional LSTM (Hochreiter and Schmidhuber, 1997) that is shared across all slots for encoding the input sequence into a sequence of hidden states (H), followed by a self-attention layer (Lin et al., 2016) to obtain a fixed dimension vector representation (c).