Unless otherwise stated we use 25 iterations of perceptron training and a beam size of 20.

The subset of size k (the beam size ) of the highest scoring expansions are retained and put back into the agenda for the next step.

Algorithm 2 Beam search and early update Input: Data set D, epochs T, beam size k: Output: weight vector to a 1: w = 0 2: fort 6 LT do

Input: Data set D, iterations T, beam size k: Output: weight vector to 1: w = 6) 2: fort 6 LT do ~ for <M¢,Ai,¢4¢> E D do Agendaa = Agendap = Aacc : 6) lossacc = 0 for j 6 Ln do ~ Agendaa = EXPAND(AgendaG, Aj, mj, k) Agendap = EXPAND(Agendap, Aj, mj, k) if fl CONTAINsCORRECT(Agendap) then 3] = EXTRACTBEST(AgendaG) g = EXTRACTBEST(Agendap) Aacc = Aacc + _ lossacc = lossacc + LOSS(jI]) Agendap = Agendaa g = EXTRACTBEST(Agendap) if fl CORRECTQQ) then g = EXTRACTBEST(AgendaG) Aace = Aacc + _ lossacc = lossacc + LOSS(:IQ) if A... 7e 6’ then update w.r.t.

the English development set as a function of number of training iterations with two different beam sizes , 20 and 100, over the local and nonlocal feature sets.

As the performance of our KB-QA system relies heavily on the k-best beam approximation, we evaluate the impact of the beam size and list the comparison results in Figure 6.

Figure 6: Impacts of beam size on accuracy.

We can see that using a small k can achieve better results than baseline, where the beam size is set to be 200.

k: beam size .

In general a larger beam size can yield better performance but increase training and decoding time.

As a tradeoff, we set the beam size as 8 throughout the experiments.

We set the beam size k to 16, which brings a good balance between efficiency and accuracy.

Input: A word-segmented sentence, beam size k. Output: A constituent parse tree.

Input: A POS-tagged sentence, beam size k. Output: A constituent parse tree.