To compute the similarity between the vector representation of the input sentences, our network uses two methods: (i) computing the similarity score obtained using a similarity matrix M (explored in (Yu et al., 2014)), and (ii) directly modelling interactions between intermediate vector representations of the input sentences via fully-connected hidden layers (used by (Hu et al., 2014)).
to the best of our knowledge, two of the most effective methods for engineering features are: (i) kernel methods, which naturally map feature vectors or directly objects in richer feature spaces; and more recently (ii) approaches based on deep learning, which have been shown to be very effective.