• Aligned question embedding: Following (Lee et al., 2016) and other recent works, the last part we incorporate is an aligned question embedding , where the attention score a i,j captures the similarity between p i and each question words q j .
a i,j is computed by the dot products between nonlinear mappings of word embeddings: , and α(•) is a single dense layer with ReLU nonlinearity.