Hence, we require an additional lookup table layer for the word overlap features LT Wo (•) with parameters W o ∈ R do×2 , where d o ∈ N is a hyper-parameter of the model, which indicates the number of dimensions used for encoding the word overlap features.
we augment word embeddings with additional dimensions that encode the fact that a given word in a pair is overlapping or semantically similar and let the network learn its optimal representation.