Therefore, 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.
we feed the function value to the decoder state to guide sentence generation: The optimal weight matrices and anchor points are obtained by minimizing the hinge loss for each sentence pair (x, y) as: Stage-wise training strategies have been proven to be efficient when system is relative complicated by plenty of recent work (Maruf and Haffari, 2018;.