Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction (AAAI 2022) Paper
Install packages by referring to pip_reqs.txt
- Movie Reviews (Paper: Pruthi et al. 2020, Weakly- and Semi-supervised Evidence Extraction, Findings of EMNLP)
- MultiRC (Paper: Khashabi et al. 2018, Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences, NAACL-HLT)
Please refer to augment_with_mask.py for data preparation. And the data folder contains the final used data.
for seed in `seq 1 1`; do
CUDA_VISIBLE_DEVICES=$GPU_ID unbuffer python main.py \
--data_dir datasets/movie_reviews_with_some_rats_adv \
--batch_size 4 \
--learning_rate 2e-5 \
--max_seq_len 512 \
--epochs 30 \
--dataset multi_rc \
--evaluate_every 10000 \
--save_extraction_model ${OUTPUT_BASE_DIR}/ \
--save_prediction_model ${OUTPUT_BASE_DIR}/ \
--include_label_embedding_features \
--seed $seed \
--upper_case \
--gradient_accumulation_steps 8 | tee -a logs/logs.txt
done;