For each word, we use the pre-trained GloVe (Pennington et al., 2014) word vector as its word embedding, and obtain its character embedding with a Convolutional Neural Network (CNN) (Kim, 2014).
for both the passage and the question, we pass the concatenation of the word embeddings and the character embeddings through a shared dense layer with ReLU activation, whose output dimensionality is d. we obtain the passage lexicon embeddings L P ∈ R d×n and the question lexicon embeddings L Q ∈ R d×m .