Then, the representations of the two arguments are concatenated to form the final representation, shown in Figure 2.
we encode each word in an argument into its word embeddings, which are fed into a BiLSTM, to get the hidden representations z i using a fully-connected layer W c on top of the concatenated hidden states We then apply an attention mechanism to induce a distribution of weights over all tokens in the argument; the final argument representation Arg is a weighted sum of z i based on the attention weights α i : Classifier The classifier consists of a single fully-connected layer on top of the encoder, finished with a softmax classification layer.