We have some parameters to tune: parsing feature weight 0p, beam size , and training epoch.

In this experiment, the external dictionaries are not used, and the beam size of 32 is used.

Table 3 shows the performance and speed of the full joint model (with no dictionaries) on CTB-Sc-l with respect to the beam size .

length, comparing with top-down, 2nd-MST and shift-reduce parsers ( beam size : 8, pred size: 5)

During training, we fixed the prediction size and beam size to 5 and 16, respectively, judged by pre-

After 25 iterations of perceptron training, we achieved 92.94 unlabeled accuracy for top-down parser with the FIRST function and 93.01 unlabeled accuracy for shift-reduce parser on development data by setting the beam size to 8 for both parsers and the prediction size to 5 in top-down parser.

The complexity becomes 0(n2 >x< b) where b is the beam size .

We used a default beam size 1:: = 10.

Table 1 shows the results of our full model using beam size 1:: = 10, as well as model variants.

As detailed, only a set of top scoring tuples of size 1:: ( beam size ) is maintained per relation 7“ E T during candidate generation.

The beam size 1:: allows controlling the tradeoff between performance and cost.

The lower bound can be initialized to the best sequence score using a beam search (with beam size being 1).

The search is similar to the beam search with beam size being 1.

We initialize the lower bound lb with the k-th best score from beam search (with beam size being 11:) at line 1.