SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task

Source code of our EMNLP 2018 paper: SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task .

Citation

@InProceedings{Yu&al.18.emnlp.syntax,
  author =  {Tao Yu and Michihiro Yasunaga and Kai Yang and Rui Zhang and Dongxu Wang and Zifan Li and Dragomir Radev},
  title =   {SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task},
  year =    {2018},  
  booktitle =   {Proceedings of EMNLP},  
  publisher =   {Association for Computational Linguistics},
}

Environment Setup

1. The code uses Python 2.7 and Pytorch 0.2.0 GPU.
2. Install Python dependency: pip install -r requirements.txt

Download Data, Embeddings, Scripts, and Pretrained Models

1. Download the dataset from the Spider task website to be updated, and put tables.json, train.json, and dev.json under data/ directory.
2. Download the pretrained Glove, and put it as glove/glove.%dB.%dd.txt
3. Download evaluation.py and process_sql.py from the Spider github page
4. Download preprocessed train/dev datasets and pretrained models from here. It contains: -generated_datasets/
   • generated_data for original Spider training datasets, pretrained models can be found at generated_data/saved_models
   • generated_data_augment for original Spider + augmented training datasets, pretrained models can be found at generated_data_augment/saved_models

Generating Train/dev Data for Modules

You could find preprocessed train/dev data in generated_datasets/.

To generate them by yourself, update dirs under TODO in preprocess_train_dev_data.py, and run the following command to generate training files for each module:

python preprocess_train_dev_data.py train|dev

Folder/File Description

• data/ contains raw train/dev/test data and table file
• generated_datasets/ described as above
• models/ contains the code for each module.
• evaluation.py is for evaluation. It uses process_sql.py.
• train.py is the main file for training. Use train_all.sh to train all the modules (see below).
• test.py is the main file for testing. It uses supermodel.sh to call the trained modules and generate SQL queries. In practice, and use test_gen.sh to generate SQL queries.
• generate_wikisql_augment.py for cross-domain data augmentation

Training

Run train_all.sh to train all the modules. It looks like:

python train.py \
    --data_root       path/to/generated_data \
    --save_dir        path/to/save/trained/module \
    --history_type    full|no \
    --table_type      std|no \
    --train_component <module_name> \
    --epoch           <num_of_epochs>

Testing

Run test_gen.sh to generate SQL queries. test_gen.sh looks like:

SAVE_PATH=generated_datasets/generated_data/saved_models_hs=full_tbl=std
python test.py \
    --test_data_path  path/to/raw/test/data \
    --models          path/to/trained/module \
    --output_path     path/to/print/generated/SQL \
    --history_type    full|no \
    --table_type      std|no \

Evaluation

Follow the general evaluation process in the Spider github page.

Cross-Domain Data Augmentation

You could find preprocessed augmented data at generated_datasets/generated_data_augment.

If you would like to run data augmentation by yourself, first download wikisql_tables.json and train_patterns.json from here, and then run python generate_wikisql_augment.py to generate more training data. Second, run get_data_wikisql.py to generate WikiSQL augment json file. Finally, use merge_jsons.py to generate the final spider + wikisql + wikisql augment dataset.

Acknowledgement

The implementation is based on SQLNet. Please cite it too if you use this code.