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Interchange Intervention Training (IIT) for Compositional Reasoning in Language Grounding

Towards Solving ReaSCAN Using Counterfactually Trained Neural Models

Release Notes

  • 10/01/2021: We are preparing to release our code.

Contents

Citation

If you use this repository, please cite the following two papers: paper for interchange intervention training, and paper for the ReaSCAN dataset.

  @article{geiger-etal-2021-iit,
        title={Inducing Causal Structure for Interpretable Neural Networks}, 
        author={Geiger, Atticus and Wu, Zhengxuan and Lu, Hanson and Rozner, Josh and Kreiss, Elisa and Icard, Thomas and Goodman, Noah D. and Potts, Christopher},
        year={2021},
        eprint={2112.00826},
        archivePrefix={arXiv},
        primaryClass={cs.LG}
  }

  @article{wu-etal-2021-reascan,
    title={Rea{SCAN}: Compositional Reasoning in Language Grounding},
    author={Wu, Zhengxuan and Kreiss, Elisa and Ong, Desmond C. and Potts, Christopher},
    journal={NeurIPS 2021 Datasets and Benchmarks Track},
    url={https://openreview.net/forum?id=Rtquf4Jk0jN},
    year={2021}}

Dataset

Off-the-shelf regenerated ReaSCAN

We use ReaSCAN framework to generate datasets for different experiments. In addition, we also use the novel direction split provided by gSCAN. For all the datasets we use, you can download them off-the-shelves from ReaSCAN-Causal.zip in this main folder.

Regenerate ReaSCAN that we use

We also provide you the full-fledged adpated ReaSCAN framework for you to generate datasets that we use in our experiments. The scripts we use are all in the experiments-meta.sh script, here is one example:

cd codes/Reason-SCAN/code/dataset/

# using ReaSCAN framework to generate datasets.
python generate_ReaSCAN.py \
--mode train \
--n_command_struct -1 \
--date 2021-09-28 \
--grid_size 6 \
--n_object_max 13 \
--per_command_world_retry_max 500 \
--per_command_world_target_count 200 \
--output_dir ../../data-files/ReaSCAN-Causal-ICLR-Official/ \
--include_relation_distractor \
--include_attribute_distractor \
--include_isomorphism_distractor \
--include_random_distractor \
--full_relation_probability 1.0 \
--command_pattern p1 \
--save_interal 50 \
--seed 42
# Note that our novel direction split is directly adapted from gSCAN
# since ReaSCAN is not supporting that yet.
# We still provide scripts to split for novel direction, but consider
# not to use it.

Models

Multimodal LSTM

This model is published with gSCAN in this paper from this repo. You can refer to their repo for details about the model. Here, we already adapt interface changes that are needed to run with ReaSCAN.

Training

We provide you training scripts for different setttings, including regular training, counterfactual training and multi-task training. We also provide you in-depth evaluation scripts that you can use to evaluate your results. You can refer to our provided scripts to see how we run our experiments in experiments-meta.sh and experiments.sh

Regular training,

cd codes/models/seq2seq/

# For regular training, you can simply run our baseline trainer.
CUDA_VISIBLE_DEVICES=0 python run_seq2seq.py \
--mode=train \
--max_decoding_steps=120 \
--max_testing_examples=2000 \
--data_directory=../../../data-files/ReaSCAN-novel-attribute/ \
--input_vocab_path=input_vocabulary.txt \
--target_vocab_path=target_vocabulary.txt \
--attention_type=bahdanau \
--no_auxiliary_task \
--conditional_attention \
--output_directory=../../../ \
--training_batch_size=200 \
--max_training_iterations=100000 \
--seed=88 \
--learning_rate 0.002 \
--is_wandb
# You can the data directory for the other task.
# --data_directory

Counterfactual training for POSITION variables,

cd codes/models/seq2seq/

# Counterfactual training for novel action sequence (direction/length).
CUDA_VISIBLE_DEVICES=0 python run_mmlstm.py \
--mode=train \
--max_decoding_steps=120 \
--max_testing_examples=2000 \
--data_directory=../../../data-files/gSCAN-novel-direction/ \
--input_vocab_path=input_vocabulary.txt \
--target_vocab_path=target_vocabulary.txt \
--attention_type=bahdanau \
--no_auxiliary_task \
--conditional_attention \
--output_directory=../../../ \
--training_batch_size=200 \
--max_training_iterations=100000 \
--seed=88 \
--learning_rate 0.002 \
--is_wandb \
--include_task_loss \
--include_cf_loss \
--include_cf_auxiliary_loss \
--intervene_dimension_size 50 \
--cf_sample_p 1.0 \
--cf_loss_weight 1.0 \
--is_wandb
# You can remove these flags to exclude losses.
# --include_task_loss
# --include_cf_loss
# --include_cf_auxiliary_loss
# You can the data directory for the other task.
# --data_directory
# You may add this option for restrict sampling of counterfactual example pairs.
# --restrict_sampling by_direction
# --restrict_sampling by_length

Counterfactual training for ATTRIBUTE variables,

cd codes/models/seq2seq/

# Counterfactual training for novel attributes.
CUDA_VISIBLE_DEVICES=0 python run_mmlstm_encoder.py \
--mode=train \
--max_decoding_steps=120 \
--max_testing_examples=2000 \
--data_directory=../../../data-files/ReaSCAN-novel-attribute/ \
--input_vocab_path=input_vocabulary.txt \
--target_vocab_path=target_vocabulary.txt \
--attention_type=bahdanau \
--no_auxiliary_task \
--conditional_attention \
--output_directory=../../../ \
--training_batch_size=200 \
--max_training_iterations=100000 \
--seed=88 \
--learning_rate 0.002 \
--include_task_loss \
--include_cf_loss \
--include_cf_auxiliary_loss \
--intervene_dimension_size 25 \
--cf_sample_p 1.0 \
--cf_loss_weight 1.0 \
--intervene_position last_hidden \
--is_wandb
# You can remove these flags to exclude losses.
# --include_task_loss
# --include_cf_loss
# --include_cf_auxiliary_loss
# You may add this option for restrict sampling of counterfactual example pairs.
# --restrict_sampling by_attribute

# Counterfactual training for novel length and direction.
CUDA_VISIBLE_DEVICES=7 python run_mmlstm.py \
--mode=train \
--max_decoding_steps=120 \
--max_testing_examples=2000 \
--data_directory=../../../data-files/gSCAN-novel-direction/ \
--input_vocab_path=input_vocabulary.txt \
--target_vocab_path=target_vocabulary.txt \
--attention_type=bahdanau \
--no_auxiliary_task \
--conditional_attention \
--output_directory=../../../ \
--training_batch_size=200 \
--max_training_iterations=100000 \
--seed=88 \
--learning_rate 0.002 \
--is_wandb \
--include_task_loss \
--include_cf_auxiliary_loss \
--intervene_dimension_size 50 \
--cf_sample_p 1.0 \
--cf_loss_weight 1.0 \
--is_wandb

Other files

We also provide other helper scripts help you to visualize datasets, split datasets and etc..

  • codes/models/seq2seq/ReaSCAN_splitter.ipynb for splitting the datasets.
  • codes/models/seq2seq/abstraction_graphical_model_demo.ipynb for demonstration for our abstract models.

License

ReaSCAN has a Creative Commons Attribution 4.0 International License.