First of all, you need to run ./prepare_data.sh to create download HotpotQA and put it in the right folder.

The code contained in this repository is divided into two parts: i) graph creation and ii) model (training and inference)

The graphs corresponding to each HotpotQA instance can be created with ./src/data/bottom_up_query_edges.py. You will need to specify the folder where you want to store the graphs in this script. I used the following folders ./data/processed/training/YOUR_FOLDER for the graphs of the training set. This scripts assumes the existence of the following files:

• List of named entities in each question (list_ent_query_training.p)
• List of named entities in each paragaph (list_hotpot_ner_no_coref_train.p)
• List of SRL args in each question (dict_ins_query_srl_triples_training.json)
• List of SRL args in each paragraph (dict_ins_doc_sent_srl_triples.json)

You can create them following ./src/data/preprocess_dataset.py (you would need to modify a bit the code to add a few lines to store these files) or download them (link at the end of this readme).

The script ./src/models/GAT_Hierar_Tok_Node_Aggr.py takes ./data/processed/training/YOUR_FOLDER (you need to specify it at the begining when loading the graphs) and trains the model. The checkpoints of the model are saved in ./models/YOUR_CHECKPOINT. This path is defined in the variable model_path.

First you need some files and pretrained models:

• Put the input file (HotpotQA dev/test set) on ./data/external/input.json
• Put the pretrained graphQA model on ./models/graph_model
• Put the SRL model link on ./models/srl_model. Do not untar it.

Now you can execute ./run.sh. This will execute the pragraph selection stage of SAE (Tu et al., 2020), then it will create the graphs for those predicted paragraphs, and lastly will inference to obtain the predicted answers.

• NER and SRL output for the training set: https://drive.google.com/file/d/1R3eyEcwYY5vj2U4X43-WrsPxpotzWS_S/view?usp=sharing

• Graphs of HotpotQA Train set: https://drive.google.com/file/d/1PiIq7lYpMTmkDddwqabbX5Nn9tnvgZ7S/view?usp=sharing
• Graphs of HotpotQA Dev set: https://drive.google.com/file/d/15mI5HrC6nZf_dMpKujC642spB82HOIaI/view?usp=sharing

• Pretrained model (BERT-based-uncased) https://drive.google.com/file/d/1ZfKgZCnLS3m7BIeT88eKTwnLe_y6rEvn/view?usp=sharing