Then, the question embedding model f consists of a word embedding layer to transform tokens into distributed representations, a two-layer BiGRU to capture the question semantics, and a linear layer to project the final hidden states of the BiGRU into the same vector space as E(r).
subject network As introduced in section 3, the factor p(s|q, r) models the fitness of a subject s appearing in the question q, given the main topic is about the relation r. two forces a) the raw context expressed by q, and b) the candidate topic described by r, jointly impact the fitness of the subject s. For simplicity, we use two additive terms to model the joint effect where u(s, r, q) is the subject scoring function, g(q) is another semantic question embedding, E(s) is a vector representation of a subject, h(r, s) is the subject-relation score, and α is the weight parameter used to trade off the two sources.