joshinh / spurious-correlations-nlp

spurious-correlations-nlp

This is the code for the paper Are All Spurious Correlations in Natural Language Alike? An Analysis through a Causal Lens which was accepted at EMNLP 2022. This code was majorly adapated from three different sources:

1. For the real NLP experiments - https://github.com/technion-cs-nlp/bias-probing
2. For the INLP experiments - https://github.com/shauli-ravfogel/nullspace_projection
3. For the toy synthetic experiments - https://github.com/cjlovering/predicting-inductive-biases

Running the code

Some of the instructions are common with the instructions from the original codebase.

1. Setting up the environment

Create a new conda environment and install libraries:

pip3 install -r requirements.txt

2. Datasets

Most of the NLP datasets are already prepared and our available at nlp/data. Some which were too large to upload (MNLI training data and MNLI synthetic training data) are uploaded here. Overall, these datasets include the MNLI training dataset, MNLI synthetic training datasets, as well as the training and evaluation datasets corresponding to the different groups (e.g. high word overlap).

3. Training

To train models either using a simple cross entropy loss on orginal/balanced dataset, or using POE/DFL, navigate to nlp/scripts and run:

python3 training.py

You can modify the training dataset, method, hyperaparameters etc. in training.yaml.

3. Probing

To run the probing, you can check out the different configs in nlp/configs. You can then run:

python3 probing.py --seed=42 --name="baseline" --task_config_file="mnli_lex_class.json" --model_name_or_path="seed:42/baseline" --overwrite_cache

4. INLP

To obtain an invariant representation using the INLP method, you can run:

python3 debias.py

The file includes path to the saved model representations which will be debiased -- you will need to modify this according to where you store it.

5. Toy Synthetic Experiments

To be updated soon!

Citation, authors, and contact

Bibtex

@inproceedings{joshi2022spurious,
        author={Nitish Joshi, Xiang Pan and He He},
        title={Are All Spurious Features in Natural Language Alike? An Analysis
through a Causal Lens},
        booktitle={EMNLP},
        year={2022}
}

Authors

Nitish Joshi, Xiang Pan and He He