Its most important advantage is that arbitrary features can be added to the model.
λ ) for Algorithm 1 1: Pre-train to obtain θ 1 = (W, W = 0, M, B) as the initial parameter 2: for all i such that 1 ≤ i ≤ M axIter do Decode with θ i on the dev set and merge all k-best-lists Run Algorithm 1 based on the merged kbest-list to obtain θ i+1 5: end for 6: Post-train based on θ M axIter+1 to obtain θ Output: θ The whole training for the AdNN model is summarized in Algorithm 2.