Official code for the paper "Context-Aware Language Modeling for Goal-Oriented Dialogue Systems"

project site | arxiv

1. create conda environment: conda create --name CALM python=3.9.7
2. activate conda environment: conda activate CALM
3. install requirements: pip install -r requirements.txt
4. install pytorch 1.9.0: conda install pytorch==1.9.0 cudatoolkit=11.3 -c pytorch -c conda-forge
5. export PYTHONPATH="$PWD/offline_airdialogue"
6. Download the processed data and model checkpoints here. The outputs/ folder contains checkpoints for our main model, our task pretrained model, and our customer bot.

(Note: all training runs use wandb by default, you can turn off wandb syncing in the config.)

• cd scripts/train

• To run data-parallel multi-GPU training, on any of the commands below replace python <script_path> with python -m torch.distributed.launch --nproc_per_node <n_GPUs> --use_env <script_path>.

• Pretraining CALM
  (two variants of the auxiliary loss function)

  • script: python train_pretrain_table_agent.py
    config: config/train_pretrain_table_agent.yaml

  • script: python train_pretrain_simplified_aux_gpt2.py
    config: config/train_pretrain_simplified_aux_gpt2.yaml

• Training the customer bot

  • script: python train_customer.py
    config: config/train_customer_bot.yaml

• Training CALM
  (two variants of the auxiliary loss function)

  • script: python train_real_table_agent.py
    config: config/train_real_table_agent.yaml

  • script: python train_simplified_aux_gpt2.py
    config: config/train_simplified_aux_agent.yaml

• Training Standard LM

  • script: python train_basic_agent.py
    config: config/train_basic_agent.yaml

• Training the reward model for Model Based Rollout Planning

  • script: python train_constraint_parser.py
    config: config/train_constraint_parser.yaml

• cd scripts/eval

• Simulated Evaluation

  • script: python selfplay_eval.py
    config: config/selfplay_eval.yaml
    
    
    • A log of results will be saved to the location specified by selfplay/outputs_file in the config. To print out the success rate for the selfplay run: python compute_results.py --results_file <your_eval_outputs_file>
      
      
    • Note: selfplay evaluation will by default use all the GPUs available on your machine. To Specify which GPUs to use, prefix the command with CUDA_VISIBLE_DEVICES=<comma_seperated_list_of_gpu_indicies>

• Language Quality Evaluation

  • script: python language_quality_eval.py
    config: config/language_eval.yaml
    
    
    • To parallelize evaluation across on multiple-GPUs, run: python -m torch.distributed.launch --nproc_per_node <n_GPUs> --use_env language_quality_eval.py