apt-get -y update
apt-get install -y unzip zip parallel
conda create -p /tmp/imrc python=3.6 numpy scipy cython nltk
conda activate /tmp/imrc
pip install --upgrade pip
pip install numpy==1.16.2
pip install gym==0.15.4
pip install tqdm pipreqs pyyaml pytz visdom
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
pip install transformers
pip install allennlp

# download SQuAD from official website, then
python utils/split_original_squad.py

python utils/preproc_squad.py

python utils/get_wiki_filter_squad.py
python utils/split_wiki_data.py

# without graph
python main.py configs/imrc_none.yaml

# co-occurrence graph
python main.py configs/imrc_cooccur.yaml

# relative position graph
python main.py configs/imrc_rel_pos.yaml

# SRL graph
python main.py configs/imrc_srl.yaml

# continuous belief graph
# in this setting, we need a pre-trained graph generator.
# we provide our pre-trained graph generator at
# https://drive.google.com/drive/folders/1zZ7C_-xaYsfg2Ms7_BO5n3Qzx69UqMKD?usp=sharing

# one can choose to train their own version by:
python pretrain_observation_infomax.py configs/pretrain_cont_bnelief.yaml
# then using the downloaded/saved model checkpoint
python main.py configs/imrc_cont_belief.yaml

general:
  naozi_capacity: 1  # capacity of agent's external memory queue (1, 3, 5)
  generate_or_point: "point"  # "qmpoint": q+o_t, "point": q, "generate": vocab
  disable_prev_next: False  # False: Easy Mode, True: Hard Mode

model:
  recurrent: True  # recurrent component described in Section 3.3 and Section 4.Additional Results

@inproceedings{Yuan2021imrc_graph,
  title={Interactive Machine Comprehension with Dynamic Knowledge Graphs},
  author={Xingdi Yuan},
  year={2021},
  booktitle="EMNLP",
}