Question: What are the two Bi-LSTM layers of the team's model?
Evidence: Figure 1 illustrates the overall architecture of the discourse-level neural network model that consists of two Bi-LSTM layers, one max-pooling layer in between and one softmax prediction layer. The input of the neural network model is a paragraph containing a sequence of discourse units, while the output is a sequence of discourse relations with one relation between each pair of adjacent discourse units.
Given the words sequence of one paragraph as input, the lower Bi-LSTM layer will read the whole paragraph and calculate hidden states as word representations, and a max-pooling layer will be applied to abstract the representation of each discourse unit based on individual word representations. Then another Bi-LSTM layer will run over the sequence of discourse unit representations and compute new representations by further modeling semantic dependencies between discourse units within paragraph. The final softmax prediction layer will concatenate representations of two adjacent discourse units and predict the discourse relation between them.
Word Vectors as Input: The input of the paragraph-level discourse relation prediction model is a sequence of word vectors, one vector per word in the paragraph. In this work, we used the pre-trained 300-dimension Google English word2vec embeddings. For each word that is not in the vocabulary of Google word2vec, we will randomly initialize a vector with each dimension sampled from the range $[-0.25, 0.25]$ . In addition, recognizing key entities and discourse connective phrases is important for discourse relation recognition, therefore, we concatenate the raw word embeddings with extra linguistic features, specifically one-hot Part-Of-Speech tag embeddings and one-hot named entity tag embeddings.
Building Discourse Unit Representations: We aim to build discourse unit (DU) representations that sufficiently leverage cues for discourse relation prediction from paragraph-wide contexts, including the preceding and following discourse units in a paragraph. To process long paragraph-wide contexts, we take a bottom-up two-level abstraction approach and progressively generate a compositional representation of each word first (low level) and then generate a compositional representation of each discourse unit (high level), with a max-pooling operation in between. At both word-level and DU-level, we choose Bi-LSTM as our basic component for generating compositional representations, mainly considering its capability to capture long-distance dependencies between words (discourse units) and to incorporate influences of context words (discourse units) in each side.
Given a variable-length words sequence $X = (x_1,x_2,...,x_L)$ in a paragraph, the word-level Bi-LSTM will process the input sequence by using two separate LSTMs, one process the word sequence from the left to right while the other follows the reversed direction. Therefore, at each word position $t$ , we obtain two hidden states $\overrightarrow{h_t}, \overleftarrow{h_t}$ . We concatenate them to get the word representation $h_t = [\overrightarrow{h_t}, \overleftarrow{h_t}]$ . Then we apply max-pooling over the sequence of word representations for words in a discourse unit in order to get the discourse unit embedding: 
$$MP_{DU}[j] = \max _{i=DU\_start}^{DU\_end}h_i[j]\quad \\
where, 1 \le j \le hidden\_node\_size$$   (Eq. 8) 
Next, the DU-level Bi-LSTM will process the sequence of discourse unit embeddings in a paragraph and generate two hidden states $\overrightarrow{hDU_t}$ and $\overleftarrow{hDU_t}$ at each discourse unit position. We concatenate them to get the discourse unit representation $hDU_t = [\overrightarrow{hDU_t}, \overleftarrow{hDU_t}]$ .
The Softmax Prediction Layer: Finally, we concatenate two adjacent discourse unit representations $hDU_{t-1}$ and $hDU_t$ and predict the discourse relation between them using a softmax function:
$$y_{t-1} = softmax(W_y*[hDU_{t-1},hDU_t]+b_y)$$   (Eq. 9) 
Figure 1 illustrates the overall architecture of the discourse-level neural network model that consists of two Bi-LSTM layers, one max-pooling layer in between and one softmax prediction layer.
Answer: One max-pooling layer in between and one softmax prediction layer.