Layerwise Anomaly

This repository contains the source code and data for our ACL 2021 paper: "How is BERT surprised? Layerwise detection of linguistic anomalies" by Bai Li, Zining Zhu, Guillaume Thomas, Yang Xu, and Frank Rudzicz.

Citation

If you use our work in your research, please cite:

Li, B., Zhu, Z., Thomas, G., Xu, Y., and Rudzicz, F. (2021) How is BERT surprised? Layerwise detection of linguistic anomalies. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL).

@inproceedings{li2021layerwise,
  author = "Li, Bai and Zhu, Zining and Thomas, Guillaume and Xu, Yang and Rudzicz, Frank",
  title = "How is BERT surprised? Layerwise detection of linguistic anomalies",
  booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL)",
  publisher = "Association for Computational Linguistics",
  year = "2021",
}

Dependencies

The project was developed with the following library versions. Running with other versions may crash or produce incorrect results.

• Python 3.7.5
• CUDA Version: 11.0
• torch==1.7.1
• transformers==4.5.1
• numpy==1.19.0
• pandas==0.25.3
• scikit-learn==0.22

Setup Instructions

1. Clone this repo: git clone https://github.com/SPOClab-ca/layerwise-anomaly
2. Download BNC Baby (4m word sample) from this link and extract into data/bnc/
3. Run BNC preprocessing script: python scripts/process_bnc.py --bnc_dir=data/bnc/download/Texts --to=data/bnc.pkl
4. Clone BLiMP repo: cd data && git clone https://github.com/alexwarstadt/blimp

GMM experiments on BLiMP (Figure 2 and Appendix A)

PYTHONPATH=. time python scripts/blimp_anomaly.py \
  --bnc_path=data/bnc.pkl \
  --blimp_path=data/blimp/data/ \
  --out=blimp_result

Frequency correlation (Figure 3 and Appendix B)

Run the notebooks/FreqSurprisal.ipynb notebook.

Surprisal gap experiments (Figure 4)

PYTHONPATH=. time python scripts/run_surprisal_gaps.py \
  --bnc_path=data/bnc.pkl \
  --out=surprisal_gaps

Accuracy scores (Table 2)

PYTHONPATH=. time python scripts/run_accuracy.py \
  --model_name=roberta-base \
  --anomaly_model=gmm

Run unit tests

PYTHONPATH=. pytest tests