Official repository containing the scripts to run the experiments of the paper "Relevance-based Infilling for Natural Language Counterfactuals".
Install the environment with Anacoda:
conda env create -f environment.yml
The subfolders in data contain instructions to download the datasets.
The black-box classifiers used for the Yelp and OLID datasets are automatically downloaded from the Hugging Face Hub. The ones used for the yelp_sentence and call_me datasets are stored in the ZENODO repository of the project (https://zenodo.org/records/8242684).
The fine-tuned Conditional Masked Language Models used in the paper are stored in the ZENODO repository as well (https://zenodo.org/records/8242684).
In this step, the feature importances are extracted from all the texts in the datasets. This can be done by running the following command:
bash scripts/extract_feature_importance.sh config/shared_configs.sh config/tasks/{dataset}/{dataset}-wrt_predicted.sh
where dataset is one of {Yelp, OLID, yelp_sentence, call_me}.
This step reads the input datasets, eventually splits them into train, validation and test set, and extracts the feature importances with respect to the class predicted by the corresponding black box.
This step fine-tunes the pretrained Conditional Masked Language Model and store the model in the results/finetuned_models/{dataset}/ folder. The command is:
bash scripts/finetune_conditional_masked_language_model.sh config/shared_configs.sh config/tasks/{dataset}/{dataset}-wrt_predicted.sh config/tasks/{dataset}/{dataset}-finetuning_config.sh
Once the Conditional Masked Language Model has been fine-tuned on the dataset, counterfactuals are generated through the following command:
bash scripts/generate_counterfactuals.sh config/shared_configs.sh config/tasks/{dataset}/{dataset}-wrt_predicted.sh config/tasks/{dataset}/{dataset}-generation_config.sh "{infilling_order}" "{direction}t"
where {infilling_order} can be chosen among [ordered, confidence], and {direction} among [left_to_right, right_to_left] when infilling_order=ordered, and {direction} among [highest_first, lowest_first] when infilling_order=confidence.
The generated counterfactuals are saved in results/generated_counterfactuals/{dataset}/.
@inproceedings{10.1145/3583780.3615029,
author = {Betti, Lorenzo and Abrate, Carlo and Bonchi, Francesco and Kaltenbrunner, Andreas},
title = {Relevance-based Infilling for Natural Language Counterfactuals},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615029},
doi = {10.1145/3583780.3615029},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {88–98},
numpages = {11},
keywords = {masked language model, explainability, counterfactuals, NLP},
location = {<conf-loc>, <city>Birmingham</city>, <country>United Kingdom</country>, </conf-loc>},
series = {CIKM '23}
}