To start, a piece of text t (t ∈ S ∪ {x}) is represented as a sequence of l hidden states, forming a feature map T ∈ R d×l , where d is the dimensionality of hidden states.
we first stack a vanilla CNN (convolution & max-pooling) (LeCun et al., 1998) over T to get a representation for t. each evidence candidate s i has a representation s i , and the claim x has a representation x.