rknaebel / discopy Goto Github PK
View Code? Open in Web Editor NEWEnd-to-end shallow discourse parser
License: MIT License
End-to-end shallow discourse parser
License: MIT License
Based on the provided implicit sense classifier, add more features such as dependency rules and word pairs for better results.
Also, it might be useful to select informative features from all extracted features (as shown in Lin et al.)
Hi! Thank you so much for this shallow discourse parser I would like to adapt it to the Ukrainian language. It would be very helpful if you give some step-by-step recommendations on how to create a JSON file, how to conduct the BERT training, and so on. It will be very helpful!
Thank you in advance!
Hi Rene,
Can I run this on GPU? CUDA_VISIBLE_DEVICES are set to empty in Dockerfile.
One more question, can I parse only explicit connectives?
This function comes from the CoNLL task. They provided a mapping from arbitrary connectives in the PDTB to their heads. This limits the application to PDTB samples and should be opened with a more general approach.
Hello @RKnaeble,
I'm reaching out to seek guidance on a couple of points related to the pdtb2
dataset:
Conversion to CoNLL Format: Could you provide insights or steps on how to convert the pdtb2
data into the CoNLL format?
Data Splits: I'm interested in understanding the train, test, and dev splits you've chosen for this dataset.
For context, the pdtb2 data structure is as follows:
0
to 23
.wsj_XXX.pdtb
.pdtb3
dataset structured with both gold
and raw
data categories.I'd appreciate any guidance or references you can provide on these topics.
Thank you for your time and assistance.
regards
Muhammed
Hi!
I would like to run your parser, but from the instructions it is not really clear what the input format is supposed to be (besides the fact that it should be json). How should I transform the PDTB data to make it work with your parser?
Thank you!
Installation problem with the new version 1.1.0
There is a long installation of dependencies installation, which doesn't give any results.
ERROR: Exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/pip/_internal/cli/base_command.py", line 180, in _main
status = self.run(options, args)
File "/usr/local/lib/python3.7/dist-packages/pip/_internal/cli/req_command.py", line 199, in wrapper
return func(self, options, args)
File "/usr/local/lib/python3.7/dist-packages/pip/_internal/commands/install.py", line 319, in run
reqs, check_supported_wheels=not options.target_dir
File "/usr/local/lib/python3.7/dist-packages/pip/_internal/resolution/resolvelib/resolver.py", line 128, in resolve
requirements, max_rounds=try_to_avoid_resolution_too_deep
File "/usr/local/lib/python3.7/dist-packages/pip/_vendor/resolvelib/resolvers.py", line 473, in resolve
state = resolution.resolve(requirements, max_rounds=max_rounds)
File "/usr/local/lib/python3.7/dist-packages/pip/_vendor/resolvelib/resolvers.py", line 384, in resolve
raise ResolutionTooDeep(max_rounds)
pip._vendor.resolvelib.resolvers.ResolutionTooDeep: 2000000
There is an error
/bin/bash: discopy-predict: command not found
Output of the command
!compgen -ac | grep discopy
is:
discopy-nn-predict
discopy-add-parses
discopy-eval
discopy-parse
discopy-train
discopy-extract
discopy-nn-parse
discopy-add-annotations
discopy-tokenize
discopy-nn-train
There is no discopy-predict
Hi,
can you please share the training data I need them for an education project,
Convert PDTB2 Data to JSON Format for Discourse Parser Training
Issue Description:
Hello Rknaebel,
I am working on adapting a discourse parser to work with the Penn Discourse Treebank version 2 (PDTB2) dataset and require assistance in converting the PDTB data into the specific JSON format used by your discourse parser.
Specific Needs:
Conversion of PDTB2 Data: I have the PDTB2 dataset in CSV format (pdtb2.csv
) as well as the WSJ texts and golden files. I need to convert these into en.train
, en.dev
, en.test
, parses.json
and relations.json
files. Could you provide guidance on the conversion process? Moreover which splits have you used in train, dev, test
Format Specifications: What are the specific format requirements for each of these files? For example, what should be the structure and headings in the relations.json
file?
Example Code or Scripts: If you have any example scripts or code snippets that could aid in this conversion process, it would be greatly beneficial.
Attempted Solutions:
However, I am still facing challenges in adapting these resources to the specific needs of the PDTB2 dataset.
Any Assistance Would Be Highly Appreciated:
Your expertise in this area would be immensely helpful for correctly formatting the PDTB2 data for use with the discourse parser.
Thank you for your time and consideration.
Best regards,
The current implementation of the argument extraction uses the inner node with the highest class probability for its prediction. Maybe there is something wrong with the implementation, e.g. features, prediction selection, etc.?
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