Implementation for NAACL-2019 paper

BAG: Bi-directional Attention Entity Graph Convolutional Network forMulti-hop Reasoning Question Answering

1. Python 3.6
2. TensorFlow >= 1.11.0
3. Pytorch >= 0.4.1
4. SpaCy >= 2.0.12 (You need to install "en" module via )
5. allennlp >= 0.7.1
6. nltk >= 3.3

And some other packages

I run it using two NVIDIA GTX1080Ti GPUs each one has 11GB memory. To run it with default batch size 32, at least 16GB memory is needed.

• Before run

You need to download pretrained 840B 300d GLoVe embeddings, and pretrained original size ELMo embedding weights and options and put them under directory /data.

• Preprocessing dataset

You need to download QAngaroo WIKIHOP dataset , unzip it and put the json files under the root directory. Then run prerpocessing script

python prepro.py {json_file_name}

It will generate four preprocessed pickle file in the root directory which will be used in the training or prediction.

• Train the model

Train the model using following command which will follow the configure in original paper

python BAG.py {train_json_file_name} {dev_json_file_name} --use_multi_gpu=true

Please make sure you have run preprocessing for both train file and dev file before training. And please make sure you have CUDA0 and CUDA1 available. If you have single GPU with more than 16GB memory, you can remove parameter --use_multi_gpu.

• Predict

After training it will put trained model onto directory /models. You can predict the answer of a json file using following command

python BAG.py {predict_json_file_name} {predict_json_file_name} --use_multi_gpu=true --evaluation_mode=true