Input: D: the initial training corpus; θ init : the initial model parameters; α: the entropy threshold of attention weight distribution; K: the maximum number of training iterations; 1: sm(x) ← ∅ 5: end for 6: for k = 1, 2..., K do 7: x ← MaskWord(x, sa(x), sm(x)) 11: if yp == y then 17: end for 24: effects, whose attention weights are expected to be decreased.
at the k-th training iteration, we adopt the following steps to deal with (x, t, y): In Step 1, we first apply the model parameters θ (k−1) of the previous iteration to generate the aspect representation v(t) (Line 9).