Hello! I have two questions regarding the processing of src and tgt files in DialogRG.

1. For src and tgt, I wonder if your work followed Zhu et. al 2019 to use the PTBTokenizer for tokenization and then applied bpe using the code from subword-nmt? I plan to run bpe like the commands below, but I'm not sure about the num_operations you used.

subword-nmt learn-bpe -s {num_operations} < {train_file} > {codes_file}
subword-nmt apply-bpe -c {codes_file} < {dev_file} > {out_file}
subword-nmt apply-bpe -c {codes_file} < {test_file} > {out_file}

2. I noticed DialogRG/dataset_utils.py also included a bert_tokenize(tokenizer, src) function. Does it mean we can also use bert_tokenizer instead of bpe on src and tgt files?

Thank you again for your reply to my previous questions!

Hi, thanks for giving instructions about the data process in #1 . However, I'm not familiar with AMR, could you give a simple real example of step 2 and step 6? For example, an origin amr graph and the processed amr graph with dummy node and speaker edges? I don't know how the format looks like.

hi！i don‘t know how to preprocess the raw data and it is not included in this project. do you have the part of this code? Looking forward to your reply.

Hi, I am trying to reproduce the preprocessing pipeline in #1, and I have a few questions:

1. For neural coreference, what setting was used? Was the speaker name(speaker1,2) included in the text? https://huggingface.co/coref/?text=Could%20I%20have%20my%20bill%2C%20please%3F%20Certainly%2C%20sir.%20I%E2%80%99m%20afraid%20there%20has%20been%20a%20mistake.What%20could%20it%20be%3F using this demo it seems to produce different result than what's in the paper.
2. For step 6, how were the edges added? were them added directly to the amr or encoded in some other way?
3. For AMR-simplifier, what's the difference between the file posted in the link and the neural-amr repo(https://github.com/sinantie/NeuralAmr)? Thanks!

Hi. I have three questions about this repository:

1. When will the preprocessed DialogRG data be uploaded?
2. DialogRE contains two versions, of which version 2 is obtained by modifying some of the annotation errors in version 1. Is it necessary to experiment on version 1?
3. Can you give some explanations about the contents of the preprocessed data, such as the contents of train.mask and train.path? This will be very helpful in understanding your code.

Thank you for your meaningful paper.

Hi, how do you transfer the DailyDialogue data to the train format, like .mask, .tgt, .rel file et; I have some confusion about relation and conception, any code for share?

Hi, @freesunshine0316 , recently I have tried to run your projects, but when I run the commond line conda create --name <env_name> --file requirements.txt, there occurences some problems as follows:

Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed

PackagesNotFoundError: The following packages are not available from current channels:
nlp=0.4.0=pypi_0
apex=0.1=pypi_0
bpemb=0.3.2=pypi_0
.......

These problems may be caused by the conda list --export, which does not capture pip-installed packages in a form that allows for recreating the environment. Resolution can be found at conda export yaml .

Thanks!