Hi, when I read the paper, I notice the words "role embedding" as follows. I read your codes carefully, but I still can't understand how to get it. Is it embedded by role_idx mapping? Moreover, I'd appreciate it if you could tell me where it is implemented in the code. Thanks!

Thanks for the excellent paper. But I have a question for the experiment setting: As a sequence labeling task, we always use transformer encoder(like BERT) to solve the NER problem, for example, the author in Doc2EDAG use BERT as the first step backbone. However, in the paper, it is said that the vanilla transformer(encoder-decoder structure) is used in NER module, which confuse me a lot. I am wondering wher the decoder part of transformer is used for? Thanks.

subprocess.CalledProcessError: Command '['/opt/anaconda3/envs/Doc2edag/bin/python', '-u', 'run_dee_task.py', '--local_rank=1', '--resume_latest_cpt', 'True', '--save_cpt_flag', 'True', '--data_dir', './Data', '--exp_dir', './Exps', '--task_name', 'try', '--num_train_epochs', '50', '--train_batch_size', '64', '--gradient_accumulation_steps', '8', '--eval_batch_size', '2', '--cpt_file_name', 'GIT']' returned non-zero exit status 1.

May I ask why this problem is? In addition, I have successfully reproduced Doc2EDAG, but the extraction results of event parameters are all numbers and cannot be visualized. How do you solve this problem

We find the released code fail to achieve the performance in paper. We observe that the max_sent_num in your code is 32 while it's 64 in previous Doc2EDAG. We wonder if it's the reason? 我们发现开源代码无法复现出论文的指标，我们观察到GIT最大句数的设置是32而Doc2EDAG是64。不知道是否是这个原因呢？

您好，在尝试用您的模型跑出结果后，得到如：./Exps/try/Output/dee_eval.test.pred_span.GIT.5.pkl的结果文件，其中一条数据是： ''' (0, [0, 0, 1, 1, 0], [None, None, [[None, None, None, None, None, None]], [[(3330, 1290), None, None, None, None, None]], None], DocSpanInfo(span_token_tup_list=[(121, 121, 121, 127, 121, 126), (3943, 3862, 5500, 819), (4507,), (3330, 1290)], span_dranges_list=[[(0, 5, 11)], [(0, 16, 20)], [(9, 0, 1)], [(12, 10, 12), (13, 8, 10)]], span_mention_range_list=[(0, 1), (1, 2), (2, 3), (3, 5)], mention_drange_list=[(0, 5, 11), (0, 16, 20), (9, 0, 1), (12, 10, 12), (13, 8, 10)], mention_type_list=[1, 3, 5, 7, 7], event_dag_info=[None, None, None, None, [{(): {None}}, {(None,): {None}}, {(None, None): {None}}, {(None, None, None): {None}}, {(None, None, None, None): {None}}, {(None, None, None, None, None): {None}}, {(None, None, None, None, None, None): {None}}, {(None, None, None, None, None, None, None): {None}}, {(None, None, None, None, None, None, None, None): {None}}]], missed_sent_idx_list=[1, 4, 7, 8, 9, 10, 12, 13, 14, 16, 19]), [None, None, [(None, None, None, None, None, None)], [(3, None, None, None, None, None)], None]) ''' 请问如何对上述数据进行解析，得到数据集中 ''' "recguid_eventname_eventdict_list": [ [ 0, "EquityPledge", { "Pledger": "李华青", "PledgedShares": "1188600股", "Pledgee": "海通证券股份有限公司", "TotalHoldingShares": "22619999股", "TotalHoldingRatio": "6.41%", "TotalPledgedShares": "18200000股", "StartDate": "2018年9月6日", "EndDate": null, "ReleasedDate": null } ], [ 1, "EquityPledge", { "Pledger": "李华青", "PledgedShares": "12151000股", "Pledgee": "海通证券股份有限公司", "TotalHoldingShares": null, "TotalHoldingRatio": "6.41%", "TotalPledgedShares": "12151000股", "StartDate": "2017年12月7日", "EndDate": null, "ReleasedDate": null } ] ] ''' 的结果？ 是否有结果解析的相关代码方便提供？ 谢谢！