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Code and data for CoNLL paper "Continual Adaptation for Efficient Machine Communication"

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hawkrobe/continual-adaptation

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conventions

  • implementation of continual learning for repeated reference is in /models/
  • interactive, web-based reference game w/ model is in /behavioral_experiments/
  • experiments on model behavior are in /listener_experiments/ and /speaker_experiments/
  • csv output from both kinds of experiments goes in /data/
  • scripts for analyzing/visualizing data from experiments are in /analysis/
  • paper is in /writing/

To reproduce experiments

  1. install dependencies and the COCO API:
conda install pytorch torchvision -c pytorch
conda install Cython nltk
pip install -U symspellpy
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI/
make
python setup.py build
python setup.py install
cd ../../
  1. clone repo and download coco annotations and images for experiments
git clone https://github.com/hawkrobe/continual-adaptation.git
sh download-coco.sh
  1. Extract pretrained model weights and vocabulary files to ./data/preprocess/

For more information on pretrained model, see tutorial here

Dependencies

Model code depends on PyTorch >=1.2.0

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Code and data for CoNLL paper "Continual Adaptation for Efficient Machine Communication"

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