DiffQRe The code is for our ACL2022 paper: On the Robustness of Question Rewriting Systems to Questions of Varying Hardness 1. Environment a. conda create --name qre python=3.7 b. pip install pytorch_lightning==0.8.0 or pip install pytorch_lightning==1.0.4 c. pip install transformers==3.3.1 2. to Obtain Adapter S on BART sh ./bin/finetune_shared.sh 3. to Train Private Models sh ./bin/finetune_shared.hard_0.sh sh ./bin/finetune_shared.hard_1.sh sh ./bin/finetune_shared.hard_2.sh 4. to Train SLAF sh ./bin/finetune.load_adapter.domain_weight_soft.sh 5. to Train SLAD sh ./bin/finetune.load_adapter.distill.sh 6. to Do Evaluation sh ./bin/eval_bleu.sh 7. Data Downloading Cannard: https://sites.google.com/view/qanta/projects/canard QReCC: https://github.com/apple/ml-qrecc