L2E

Code implementation for paper Learning to Explain: Generating Stable Explanations Fast at ACL 2021, by Xuelin Situ, Ingrid Zukerman, Cecile Paris, Sameen Maruf and Reza Haffari.

Requirements and Installation

• Python version >= 3.6.8
• PyTorch version >= 1.7.0
• HuggingFace transformers version >= 1.2.0
• LIME >= 0.1.1.36
• shap == 0.29.3

Experiments (steps to replicate the results from the paper)

1. Collect explanations from different baselines >> preprocess.collect_base_explanations.py

2. Train L2E explainer (also refer to folder hyperparameters) >> learning2explain.py

3. Find neighbours for each test example (for stability evaluation):

   • For IMDB_R >> evaluation.find_neighbours_imbdr.py
   • For other datasets >> evaluation.find_neighbours.py

4. Faithfulness evaluation:

   • Prediction based >> evaluation.compare_faithfulness_agreement.py
   • Confidence based >> evaluation.compare_faithfulness.py
   • Prcision/Recall (for IMDB_R only) >> evaluation.compare_imdbr_faithfulness.py

5. Stability evaluation:

   • For IMDB_R >> evaluation.compare_imdbr_stability.py
   • For other datasets >> evaluation.compare_stability.py

6. Efficiency evaluation >> compare_efficiency.py