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README.md

Module networks for question answering on knowledge graph

This code repository contains a TensorFlow model for question answering on knowledge graph with end-to-end module networks. The original paper describing end-to-end module networks is as follows.

R. Hu, J. Andreas, M. Rohrbach, T. Darrell, K. Saenko, Learning to Reason: End-to-End Module Networks for Visual Question Answering. in arXiv preprint arXiv:1704.05526, 2017. (PDF)

@article{hu2017learning,
  title={Learning to Reason: End-to-End Module Networks for Visual Question Answering},
  author={Hu, Ronghang and Andreas, Jacob and Rohrbach, Marcus and Darrell, Trevor and Saenko, Kate},
  journal={arXiv preprint arXiv:1704.05526},
  year={2017}
}

The code in this repository is based on the original implementation for this paper.

Requirements

  1. Install TensorFlow 1.0.0. Follow the official guide. Please note that newer or older versions of TensorFlow may fail to work due to incompatibility with TensorFlow Fold.
  2. Install TensorFlow Fold. Follow the setup instructions. TensorFlow Fold only supports Linux platform. We have not tested the code on other platforms.

Data

  1. Download the MetaQA dataset. Click the button MetaQA and then click Download in the drop-down list. Extract the zip file after downloading completed. Read the documents there for dataset details.
  2. Move the MetaQA folder to the root directory of this repository.

How to use this code

We provide an experiment folder exp_1_hop, which applies the implemented model to the 1-hop vanilla dataset in MetaQA. More experiment folders are coming soon.

Currently, we provide code for training with ground truth layout, and testing the saved model. Configurations can be modified in config.py. They can also be set via command line parameters.

To train the model:

python exp_1_hop/train_gt_layout.py

To test the saved model (need to provide the snapshot name):

python exp_1_hop/test.py --snapshot_name 00010000

Model introduction

  1. In this model, we store the knowledge graph in a key-value based memory. For each knowledge graph edge (subject, relation, object), we use the (subject, relation) as the key and the object as the value.
  2. All entities and relations are embedded as fixed-dimension vectors. These embeddings are also end-to-end learned.
  3. Neural modules can separately operate on either the key side or the value side.
  4. The attention is shared between keys and corresponding values.
  5. The answer output is based on the attention-weighted sum over keys or values, depending on the output module.

Contact

Authors: Yuyu Zhang, Xin Pan

Pull requests and issues: @yuyuz