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SaLoss

Repository for the paper "Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience", G.Chrysostomou and N.Aletras, to appear at EMNLP2021. Pre-print available at this link

Prerequisites

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

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

Downloading Task Data

You can run the jupyter notebooks found under tasks/task_name/*ipynb to generate a filtered, processed csv file used then by the dataloader to train the models.

Training and Evaluating the models

We include an example_script.txt were by you can see how to run experiments for baseline and saloss (ours). For more details see the sections below.

Training on full text

You can train the models on full text using the following options:

  • dataset : {"sst", "agnews", "evinf", "multirc", "semeval"}
  • data_dir : directory where task data is
  • model_dir : directory for saved models
  • saliency_scorer : {None, "textrank", "tfidf","chisquared", "uniform"}

and running the following script:

for seed in 100 200 300
do
python train_on_full.py -dataset $task_name \
                        -data_dir $data_dir \
                        -model_dir $model_dir \ 
                        --seed $seed 
done
python train_on_full.py -dataset $task_name \
                        -data_dir $data_dir \
                        -model_dir $model_dir \ 
                        --evaluate_models

simply add --saliency_scorer $sal_scorer if you want to add a particular saliency scorer in the above and ANY of the following.

Extracting rationales

  • extracted_rationale_dir : directory where to store extracted rationales and importance scores
python extract_rationales.py -dataset $task_name \
                             -data_dir $data_dir \
                             -model_dir $model_dir \
                             -extracted_rationale_dir $extract_rat_dir

Evaluating on frac of tokens

  • evaluation_dir : directory where to save frac of results
python evaluate_on_flips.py -dataset $task_name \
                            -data_dir $data_dir \
                            -model_dir $model_dir \
                            -evaluation_dir $evaluation_dir

Training on rationales

It is important to train on rationales using the following argument --train_on_rat

  • importance_metric : {"attention", "scaled_attention", "gradients", "ig"}
  • thresholder : {topk, contigious}
for seed in 100 200 300
do
python train_on_full.py -dataset $task_name \
                        -data_dir $rat_data_dir \
                        -model_dir $rat_model_dir \
                        --seed $seed \
                        --train_on_rat  \
                        --importance_metric "attention" \
                        --thresholder $thresh
done
python train_on_full.py -dataset $task_name \
                        -data_dir $rat_data_dir \
                        -model_dir $rat_model_dir \
                        --train_on_rat  \
                        --importance_metric "attention" \
                        --thresholder $thresh \
                        --evaluate_models

Summarising results

To create the tables and figures seen in the paper you can run the generate_results.py script.