https://github.com/zcccccz/causalwalk

Code and data for our paper "Causal Walk: Debiasing Multi-hop Fact Verification with Front-door Adjustment" in AAAI 2024.
https://github.com/zcccccz/causalwalk

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Code and data for our paper "Causal Walk: Debiasing Multi-hop Fact Verification with Front-door Adjustment" in AAAI 2024.

• Host: GitHub
• URL: https://github.com/zcccccz/causalwalk
• Owner: zcccccz
• License: mit
• Created: 2023-12-12T05:26:11.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-30T10:28:48.000Z (about 2 years ago)
• Last Synced: 2024-05-30T12:04:56.396Z (about 2 years ago)
• Language: Python
• Size: 501 KB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Causal Walk

Code and data for our paper "[Causal Walk: Debiasing Multi-hop Fact Verification with Front-door Adjustment](https://doi.org/10.1609/aaai.v38i17.29925)" in AAAI 2024.

![CausalWalk](CausalWalk.svg)

# Download

To reproduce the results in the paper, you need to download the dataset and our pre-trained model from [here](https://drive.google.com/drive/folders/19IdPsf0hZCnoHVnfHIelzEmnbkU3EF_6?usp=sharing).

Then you should organize them in the following format.

```

CausalWalk

    ├── data

    ├── pretrained_models

    ├── outputs

    ├── data_load_utils.py

    ├── models.py

    ├── train_causal_walk_fever_2way.py

    ├── train_causal_walk_politihop_2way.py

    ├── train_causal_walk_politihop_3way.py

    ├── train.sh

    └── utils.py

```

# Environment

Install python dependencies.

```

pip install -r requirements.txt

```

# Reproduction

Trained on the FEVER dataset and evaluated on Adversarial FEVER. (2-way)

```

CUDA_VISIBLE_DEVICES="0" python train_causal_walk_fever_2way.py \

--seed 1234 \

--batch_size 16 \

--lr 2e-5 \

--epochs 20 \

--weight_decay 5e-4 \

--evi_num 5 \

--max_seq_length 128

```

Trained on the PolitiHop dataset and evaluated on Adversarial PolitiHop. (3-way)

```

CUDA_VISIBLE_DEVICES="0" python train_causal_walk_politihop_3way.py \

--seed 1234 \

--batch_size 4 \

--lr 1e-5 \

--epochs 20 \

--weight_decay 5e-4 \

--evi_num 20 \

--max_seq_length 128 

```

Trained on the PolitiHop dataset and evaluated on Symmetric PolitiHop. (2-way)

```

CUDA_VISIBLE_DEVICES="0" python train_causal_walk_politihop_2way.py \

--seed 1234 \

--batch_size 4 \

--lr 1e-5 \

--epochs 10 \

--weight_decay 5e-4 \

--evi_num 20 \

--max_seq_length 128 

```

# Citation

```

@inproceedings{zhang2024causal,

  title={Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment},

  author={Zhang, Congzhi and Zhang, Linhai and Zhou, Deyu},

  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},

  volume={38},

  number={17},

  pages={19533--19541},

  year={2024}

}

```