We adopt an independent deep transition RNN named global contextual encoder (right part in Figure 1) to capture global features.
we transform the hidden states of global contextual encoder into a fixed-size vector with various strategies, such as mean pooling, max pooling and self-attention mechanism (Vaswani et al., 2017).