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Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games

Findings of ACL 2023

The old website for our dataset may be down. You can directly download the dataset via this link. Please read README in this link for dowload guidance.

This repo contains codes for the following paper:

Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James M. Rehg, Diyi Yang: Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games

If you find our work is helpful to your research, please use the bibtex below to cite the paper.

@inproceedings{lai2023werewolf,
  title={Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games},
  author={Lai, Bolin and Zhang, Hongxin and Liu, Miao and Pariani, Aryan and Ryan, Fiona and Jia, Wenqi and Hayati, Shirley Anugrah and Rehg, James and Yang, Diyi},
  booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
  pages={6570--6588},
  year={2023}
}

Usage

Install dependency

conda env create -f env.yaml
conda activate PersuasionGames

Run

Run multiple Experiments

We provide script exp.sh to run hyperparameter search for bert model.

Then you can use utils.py to gather the results and have the best performing hyper-parameters according to their dev results.

Then you can run exp_context.sh with the best hyperparameter to experiment on different context sizes.

Single Run

CUDA_VISIBLE_DEVICES=0 python3 baselines/main.py --output_dir out

Optional parameters:

  • model_type (only bert and roberta available now, please be careful if you are adding other models)
  • model_name
  • batch_size
  • learning_rate
  • num_train_epochs
  • seed
  • avalon
    • controlling whether to evaluate on avalon set
  • video
    • controlling whether to use video features

Result

Results will be shown in the folder you assigned as output_dir (out by default)

I've uploaded some results along with the training curves which can be visualized with tensorboard --logdir out