Closed Fantabulous-J closed 3 years ago
@Fantabulous-J Sorry, not available yet. Here is an example training cmd for training the model from scratch.
nohup python -u -m supar.cmds.biaffine_semantic_dependency train -b -d 0 -p model > model.train.log.verbose 2>&1 &
Thanks for your reply. Is the model trained on SemEval 2015 Task 18 English datasets?
@Fantabulous-J Yeah, the same dataset as used in Dozat et al. 2018 and Wang et al. 2019.
Could you please tell me how to get the training data? It seems that I can't find any information about datasets on the official website and I am not sure whether this dataset is publicly accessible.
@Fantabulous-J My data comes from LDC. Not sure if it is publicly available on the Internet.
@yzhangcs I have got the dataset from LDC and is the file "en.dm.sdp" used in the model training? How should I covert it to train.conllu et al?
@Fantabulous-J This is a toy example
#20001001
1 Pierre Pierre _ NNP _ 2 nn _ _
2 Vinken _generic_proper_ne_ _ NNP _ 9 nsubj 1:compound|6:ARG1|9:ARG1 _
3 , _ _ , _ 2 punct _ _
4 61 _generic_card_ne_ _ CD _ 5 num _ _
5 years year _ NNS _ 6 npadvmod 4:ARG1 _
6 old old _ JJ _ 2 amod 5:measure _
7 , _ _ , _ 2 punct _ _
8 will will _ MD _ 9 aux _ _
9 join join _ VB _ 0 root 0:root|12:ARG1|17:loc _
10 the the _ DT _ 11 det _ _
11 board board _ NN _ 9 dobj 9:ARG2|10:BV _
12 as as _ IN _ 9 prep _ _
13 a a _ DT _ 15 det _ _
14 nonexecutive _generic_jj_ _ JJ _ 15 amod _ _
15 director director _ NN _ 12 pobj 12:ARG2|13:BV|14:ARG1 _
16 Nov. Nov. _ NNP _ 9 tmod _ _
17 29 _generic_dom_card_ne_ _ CD _ 16 num 16:of _
18 . _ _ . _ 9 punct _ _
#20001002
1 Mr. Mr. _ NNP _ 2 nn _ _
2 Vinken _generic_proper_ne_ _ NNP _ 4 nsubj 1:compound|3:ARG1 _
3 is is _ VBZ _ 4 cop 0:root _
4 chairman chairman _ NN _ 0 root 3:ARG2|5:ARG1 _
5 of of _ IN _ 4 prep _ _
6 Elsevier _generic_proper_ne_ _ NNP _ 7 nn 5:ARG2|7:compound|12:appos _
7 N.V. N.V. _ NNP _ 5 pobj _ _
8 , _ _ , _ 7 punct _ _
9 the the _ DT _ 12 det _ _
10 Dutch Dutch _ JJ _ 12 nn _ _
11 publishing publish _ NN _ 12 amod _ _
12 group group _ NN _ 7 appos 9:BV|10:ARG1|11:compound _
13 . _ _ . _ 4 punct _ _
You should merge lines with the same ID into a single one, in which the anwsers are separated by '|'.
@yzhangcs Thanks for answering my question. Do you have any recommended preprocessing code? Did you use the code in https://github.com/tdozat/Parser-v3?
Hi, thanks for sharing such amazing work. May I ask if the off-the-shelf semantic dependency parser available? Or if it's not available, how can I train from scratch by myself?