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DCGCN

The model requires:

• Python3
• MXNet 1.3.0
• Sockeye 1.18.56
• CUDA

If you want to run sockeye on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings. Depending on your version of CUDA you can do this by running the following:

> pip install -r requirements/requirements.gpu-cu${CUDA_VERSION}.txt
> pip install .

where ${CUDA_VERSION} can be 75 (7.5), 80 (8.0), 90 (9.0), 91 (9.1), or 92 (9.2).

The preprocessed ENT-DESC dataset is saved in ./sockeye/data. For more details regarding the data preparation step, please refer to ENT-DESC.

Before that, we need to convert the raw dataset into multi graphs for training. For details please refer to the paper.

To train the DCGCN model, run:

./train.sh

Model checkpoints and logs will be saved to ./sockeye/model.

When we finish the training, we can use the trained model to decode on the test set, run:

./decode.sh

This will use the last checkpoint by default. Use --checkpoints to specify a model checkpoint file.

For BLEU score evaluation, run:

python3 -m sockeye.evaluate -r sockeye/data/ENT-DESC\ dataset/test_surface.pp.txt  -i sockeye/data/ENT-DESC\ dataset/test.snt.out

@inproceedings{cheng2020ent,
  title={ENT-DESC: Entity Description Generation by Exploring Knowledge Graph},
  author={Cheng, Liying and Wu, Dekun and Bing, Lidong and Zhang, Yan and Jie, Zhanming and Lu, Wei and Si, Luo},
  booktitle={Proceedings of EMNLP},
  year={2020}
}