ShadowGNN

Code for our NAACL'21 accepted paper: ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

Environment Setup

• Python3.6

Install Python dependency via pip install -r requirements.txt when the environment of Python and Pytorch is setup.

Running Code

Generating train/dev data by yourself

You could process the origin Spider Data by your own. Download and put train.json, dev.json and tables.json under ./data/ directory and follow the instruction on ./preprocess/

Training

Run train.sh to train ShadowGNN.

sh train.sh

Testing

Run eval.sh to eval ShadowGNN.

sh eval.sh

Evaluation

You could follow the general evaluation process in Spider Page

Citation

If you use ShadowGNN, please cite the following work.

@inproceedings{chen2021shadowgnn,
  title={ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser},
  author={Chen, Zhi and Chen, Lu and Zhao, Yanbin and Cao, Ruisheng and Xu, Zihan and Zhu, Su and Yu, Kai},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={5567--5577},
  year={2021}
}

Thanks

We would like to thank Tao Yu and Bo Pang for running evaluations on our submitted models. We are also grateful to the flexible semantic parser TranX and IRNet for their released nl2sql decoding module.