Repository for the paper "On the Impact of Temporal Concept Drift on Model Explanations" (EMNLP2022).

Prerequisites

Install necessary packages by using the files conda_reqs.txt and pip_reqs.txt

conda create --name ood_faith --file  conda_reqs.txt
conda activate ood_faith
pip install -r pip_reqs.txt
python -m spacy download en_core_web_sm

Downloading Task Data

Run the following script from this directory:

python src/data_functions/data_processors.py --data_directory "datasets"

This script downloads temporary data related to our datasets, and processes them and saves them in a json format in --data_directory. Also generates a description of the data splits with related data statistics.

Training Models

You can train the following models with train_fulltext_and_kuma.py script:

(1) BERT-base on full-text (default option --> inherently_faithful == None)

(2) bi-LSTM on full-text (inherently_faithful == "full_lstm")

(3) HardKuma models (inherently_faithful == "kuma")

(4) Lei et. al. models (inherently_faithful == "rl")

, using the following options:

• dataset : {"SST","IMDB", "Yelp", "AmazDigiMu", "AmazPantry", "AmazInstr"}

• data_dir : directory where task data is

• model_dir : directory for saving trained models

• seed : random seed for the experiment

• evaluate_models : used for evaluating trained models on test set

• inherently_faithful : {"kuma", "rl", "full_lstm", None}

for seed in 5 10 15 20 25
do	
    python train_fulltext_and_kuma.py 
                                    --dataset SST 
                                    --data_dir data/ 
                                    --model_dir models/ 
                                    --seed $seed
done    
python train_fulltext_and_kuma.py 
                            --dataset SST 
                            --data_dir data/ 
                            --model_dir models/ 
                            --evaluate_models

Using SPECTRA to extrat rationalization

Please refer to the original SPECTRA repo To run different seeds:

python -m rationalizers train --config configs/cus/seed5.yaml
python -m rationalizers train --config configs/cus/seed10.yaml
python -m rationalizers train --config configs/cus/seed15.yaml
python -m rationalizers train --config configs/cus/seed20.yaml
python -m rationalizers train --config configs/cus/seed25.yaml

to extract rationales

data="AmazDigiMu" # example

shopt -s nullglob


for i in $(find ./experiments/$data/ -type f -iname "*.ckpt"); do
echo $i
python -m rationalizers predict --config configs/cus/seed25.yaml --ckpt $i # example
done

Evaluating post-hoc explanation faithfulness

You can run sufficiency and comprehensiveness tests using the evaluate_posthoc.py script, using the following options:

• dataset : {"SST","IMDB", "Yelp", "AmazDigiMu", "AmazPantry", "AmazInstr"}

• data_dir : directory where task data is

• model_dir : directory for saving trained models

• evaluation_dir : directory for saving faithfulness results

• thresholder : {"topk", "contigious"}

• inherently_faithful : {None}

python evaluate_posthoc.py 
	    --dataset SST 
	    --data_dir data/ 
	    --model_dir models/ 
	    --evaluation_dir posthoc_results/
	    --thresholder "topk" 

Extracting rationales for FRESH

You can extract rationales from all feature attributions using the FRESH_extract_rationales.py script, using the following options:

• dataset : {"SST","IMDB", "Yelp", "AmazDigiMu", "AmazPantry", "AmazInstr"}

• data_dir : directory where task data is

• model_dir : directory for saving trained models

• extracted_rationale_dir : directory to save extracted_rationales

• thresholder : {"topk", "contigious"}

  Example script:

  python FRESH_extract_rationales.py 
  	    --dataset SST 
  	    --data_dir data/ 
  	    --model_dir models/ 
  	    --extracted_rationale_dir extracted_rationales/
  	    --thresholder "topk" 

Training FRESH classifier

You can train a Bert-base classifier on the rationales with FRESH_train_on_rationales.py script, using the following options:

• dataset : {"SST","IMDB", "Yelp", "AmazDigiMu", "AmazPantry", "AmazInstr"}
• extracted_rationale_dir : directory where extracted rationales are
• rationale_model_dir : directory for saving trained FRESH classifier
• seed : random seed for the experiment
• evaluate_models : used for evaluating trained models on test set
• importance_metric : {"attention", "gradients", "scaled attention", "ig", "deeplift"}
• thresholder : {"topk", "contigious"}

Example script:

feature_attribution="scaled attention"

for seed in 5 10 15 20 25
do	
    python FRESH_train_on_rationales.py 
                    --dataset SST 
                    --extracted_rationale_dir extracted_rationales/ 
                    --rationale_model_dir FRESH_classifiers/ 
                    --thresholder "topk"
                    --seed $seed
                    --importance_metric $feature_attribution
done    
python FRESH_train_on_rationales.py 
                    --dataset SST 
                    --data_dir data/ 
                    --model_dir models/ 
                    --extracted_rationale_dir extracted_rationales/ 
                    --rationale_model_dir FRESH_classifiers/ 
                    --thresholder "topk"
                    --seed $seed
                    --importance_metric $feature_attribution
                    --evaluate_models