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Code for Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion (ACL 2024 Findings)

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1. Prepare environment

python==3.8.0
pytorch==1.7.1
scikit-learn==1.0.2
gensim==4.3.0
pyyaml==6.0
tqdm

Notice: Fix the bug of invalid values of coherence models in gensim following RaRe-Technologies/gensim#3040.

2. Train and evaluate the model

We provide a shell script under ./CFDTM/scripts/run.sh to train and evaluate our model.
Change to directory ./CFDTM, and run command as

./scripts/run.sh NYT 50

Other datasets are available in TopMost.

Citation

If you want to use our code, please cite as

@inproceedings{wu2024dynamic,
    title = "Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion",
    author = "Wu, Xiaobao  and Dong, Xinshuai  and Pan, Liangming  and Nguyen, Thong  and Luu, Anh Tuan",
    editor = "Ku, Lun-Wei  and Martins, Andre  and Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.183",
    pages = "3088--3105"
}