yikangshen / prpn Goto Github PK

for parsing need Penn Treebank Parsed data.
how can I start parsing training from scratch with plain text?

Hi,
I am trying to verify the experiment results presented in the original paper. I made some changes to the code in order to perform the ablation test. My code is on https://github.com/duanqn/PRPN/tree/LMparse
diff: master...duanqn:LMparse
I'm doing the first ablation test, which is to remove parsing network and replace structured attention with normal ones.
I have run the original code on Penn Treebank dataset, getting 61.53 perplexity (which is similar to 61.98 in the paper). After removing the parsing network, I got 62.18 test PPL which is different from 64.42 in the paper.
Right now I'm not sure if I modified the network correctly. Could you please show me the right way to change the structure of the network?

Thanks,
Qingnan

Hi,
I am trying to compute right-branching and upper bound baselines on the WSJ10 dataset. When I use the code in prpn to evaluation, I get 56.68 (right-branching) and 84.06 (upper bound), different from 61.7(RBranch) 88.1(upper bound) in the paper. But when I use EvalB to do the evaluation, I get 61.7(RBranch) 88.1(upper bound).
So is that mean the way to evaluate the right-branching structure is different from prpn model? Could you please show the right way to compute right-branching and upper bound baselines on the WSJ10 dataset?

Thanks,
Yunfan