However, it is impossible to store the weight and bias for every q t computed within the training data.
we approximate the weight and bias function in Eq.18 using local coordinate coding as: where v j ∈ C is an anchor point, W v j and b v j are trainable parameters corresponding to v j , and γ j (q t ) is the weight function, computed as: Similar to M-RefNet, the score is computed by the tri-nonlinear function as: Here, f s (q t ) can be regarded as an approximation of e(y t ) based on all the sentence pairs in the training data.