GraphFlow

Code & data accompanying the IJCAI 2020 paper "GRAPHFLOW: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension"

Get started

Prerequisites

This code is written in python 3. You will need to install a few python packages in order to run the code. We recommend you to use virtualenv to manage your python packages and environments. Please take the following steps to create a python virtual environment.

• If you have not installed virtualenv, install it with pip install virtualenv.
• Create a virtual environment with virtualenv venv.
• Activate the virtual environment with source venv/bin/activate.
• Install the package requirements with pip install -r requirements.txt.

Run the model

• Download the preprocessed data from here and put the data folder under the root directory. (Note: if you cannot access the above data, please download from here.)

• Run the model

   python main.py -config config/graphflow_dynamic_graph_coqa.yml
  

Prepare your own data

• Download the raw data

   sh download.sh
  

• Run the stanford-core-nlp script

  check out https://stanfordnlp.github.io/CoreNLP/corenlp-server.html

• Run the preprocessing script

   python coqa_scripts/preprocess.py -d path_to_input_data -o path_to_output_data
  

• Annotate the data if you want to have the input passage represented as graph-structured data

   python annotate_graphs.py -i path_to_input_data -o path_to_output_data
  

Reference

If you found this code useful, please consider citing the following paper:

Yu Chen, Lingfei Wu, Mohammed J. Zaki. "Graphflow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension." In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, Jul 11-17, 2020.

@article{chen2019graphflow,
  title={Graphflow: Exploiting conversation flow with graph neural networks for conversational machine comprehension},
  author={Chen, Yu and Wu, Lingfei and Zaki, Mohammed J},
  journal={arXiv preprint arXiv:1908.00059},
  year={2019}
}