Code for our ACL'19 accepted paper: Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

• Python3.6
• Pytorch 0.4.0 or higher

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

• Download Glove Embedding and put glove.42B.300d under ./data/ directory
• Download Pretrained IRNet and put IRNet_pretrained.model under ./saved_model/ directory
• Download preprocessed train/dev datasets from here and put train.json, dev.json and tables.json under ./data/ directory

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/

Run train.sh to train IRNet.

sh train.sh [GPU_ID] [SAVE_FOLD]

Run eval.sh to eval IRNet.

sh eval.sh [GPU_ID] [OUTPUT_FOLD]

You could follow the general evaluation process in Spider Page

If you use IRNet, please cite the following work.

@inproceedings{GuoIRNet2019,
  author={Jiaqi Guo and Zecheng Zhan and Yan Gao and Yan Xiao and Jian-Guang Lou and Ting Liu and Dongmei Zhang},
  title={Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation},
  booktitle={Proceeding of the 57th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year={2019},
  organization={Association for Computational Linguistics}
}

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 that inspires our works.

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