At the ith step (0 < i ≤ K), a state item candidate i is generated by extending an existing state item candidate i−1 on the agenda with δ(candidate i , i).
in this case, we have when a state item is extended, its score can be updated by adding the incremental score of the step Φ(δ(candidate i , i)) • w. The nature of the scoring function means that, given appropriately defined features, it can be computed efficiently for both the incremental decoding and training processes.