Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks

Code for the ACL 2023 paper "Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks"

If any questions, please contact the email: [email protected]

1 Requirement

Our working environment is Python 3.8. Before you run the code, please make sure you have installed all the required packages. You can achieve it by simply execute the shell as sh requirements.sh

Then you should prepare embedding, xlnet, and stanza:

1. Download embedding from here and put it under the folder "data/embedding".
2. Download xlnet-base_cased from here and put it under the folder "data/pretrained_models".
3. Download stanza resource via python3 preprocessing.py and put it under the folder "data/stanza".

2 GCDC

To run experiments on GCDC, you should:

1. Put the raw corpora under the folder "data/dataset/raw/gcdc"
2. Convert raw data into json files via python3 preprocessing.py
3. Call the script. For example, you can sh script/run_clinton.sh to run experiments on gcdc_clinton.

3 Toefl

To run experiments on Toefl, you should:

1. Put the raw corpora under the folder "data/dataset/raw/toefl"
2. Convert raw data into json files via python3 preprocessing.py
3. Call the script. For example, you can sh script/run_toefl1.sh to run experiments on the prompt 1 of toefl corpus.

4 Citation

@inproceedings{liu-etal-2023-modeling,
    title = "Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks",
    author = "Liu, Wei  and
      Fu, Xiyan  and
      Strube, Michael",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.431",
    pages = "7792--7808",
}