Tetris

Source code for the paper Incorporating Task-Specific Concept Knowledge into Script Learning (EACL 2023)

Environments

• Ubuntu-18.0.4
• Python (3.7)
• Cuda (11.1)

Installation

Install Pytorch 1.9.0, then run the following in the terminal:

cd code # get into NS/code/ 
conda create -n tetris python=3.7 -y  # create a new conda environment
conda activate tetris

chmod +x scripts/setup.sh
./scripts/setup.sh

Then put data/ folder from Google Drive into code/

Note

The running of the system might require wandb account login

Training

Enter the code/ directory

To directly run on all data categories without contrastive learning, you can run the following (will take > 20hrs)

chmod +x scripts/*
./scripts/run_bsl.sh # for baseline
./scripts/run_ours.sh # for our methods

Run one of the following for an individual model:

Change $_datamode to one of cv, fb, or food. For the retrieval method, change $_topk_prompt to one of 1, 2, or 3 for different number of neighbors

Note: To run SOCL, add --do_cl 1 and --cl_exp_type a/b (a or b), change --model_name to sl, and change --lr and --num_epochs accordingly

# baseline
python main.py \
    --use_cache 0  \
    --plm_lr 3e-5 \
    --lr 3e-5 \
    --plm "bart-base" \
    --batch_size 16 \
    --eval_batch_size 16 \
    --no_dl_score 0 \
    --patience 15 \
    --num_epochs 25 \
    --num_evals_per_epoch 2 \
    --exp_msg "bsl"  \
    --data_mode $_datamode \
    --pred_with_gen 1

# method 1 retrieval (CRA)
python main.py \
  --use_cache 0 \
  --plm_lr 3e-5 \
  --lr 3e-5 \
  --plm "bart-base" \
  --batch_size 16 \
  --eval_batch_size 16 \
  --num_epochs 25 \
  --num_evals_per_epoch 2 \
  --no_dl_score 0 \
  --patience 15 \
  --use_generated_factors 1 \
  --exp_msg "method 1" \
  --data_mode $_datamode \
  --silver_eval 0 \
  --use_pretrained_concepts 0 \
  --factor_expander 1 \
  --train_gold_concepts 0 \
  --eval_gold_concepts 0 \
  --topk_prompt $_topk_prompt \
  --pred_with_gen 1

# method 2 use output from concept generator (CG)
python main.py \
    --use_cache 0 \
    --plm_lr 3e-5 \
    --lr 3e-5 \
    --plm "bart-base" \
    --batch_size 16 \
    --eval_batch_size 16 \
    --num_epochs 25 \
    --num_evals_per_epoch 2 \
    --no_dl_score 0 \
    --patience 15 \
    --use_generated_factors 1 \
    --exp_msg "method 2" \
    --data_mode $_datamode \
    --silver_eval 0 \
    --use_pretrained_concepts 1 \
    --factor_expander 1 \
    --train_gold_concepts 0 \
    --eval_gold_concepts 0 \
    --topk_prompt 1 \
    --pred_with_gen 1

# Gold-Concepts Variant
python main.py \
    --use_cache 0 \
    --plm_lr 3e-5 \
    --lr 3e-5 \
    --plm "bart-base" \
    --batch_size 16 \
    --eval_batch_size 16 \
    --num_epochs 25 \
    --num_evals_per_epoch 2 \
    --no_dl_score 0 \
    --patience 15 \
    --use_generated_factors 1 \
    --exp_msg "method gold" \
    --data_mode $_datamode \
    --silver_eval 0 \
    --use_pretrained_concepts 0 \
    --factor_expander 1 \
    --train_gold_concepts 1 \
    --eval_gold_concepts 1 \
    --topk_prompt 1 \
    --pred_with_gen 1