RAT-SQL

This repository contains code for the ACL 2020 paper "RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers".

If you use RAT-SQL in your work, please cite it as follows:

@inproceedings{rat-sql,
    title = "{RAT-SQL}: Relation-Aware Schema Encoding and Linking for Text-to-{SQL} Parsers",
    author = "Wang, Bailin and Shin, Richard and Liu, Xiaodong and Polozov, Oleksandr and Richardson, Matthew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    pages = "7567--7578"
}

Usage

Step 1: Download third-party datasets & dependencies

Download the datasets: Spider and WikiSQL. Unpack them somewhere outside this project to create the following directory structure:

/path/to/data
├── spider
│   ├── database
│   │   └── ...
│   ├── dev.json
│   ├── dev_gold.sql
│   ├── tables.json
│   ├── train_gold.sql
│   ├── train_others.json
│   └── train_spider.json
└── wikisql
    ├── dev.db
    ├── dev.jsonl
    ├── dev.tables.jsonl
    ├── test.db
    ├── test.jsonl
    ├── test.tables.jsonl
    ├── train.db
    ├── train.jsonl
    └── train.tables.jsonl

To work with the WikiSQL dataset, clone its evaluation scripts into this project:

mkdir -p third_party
git clone https://github.com/salesforce/WikiSQL third_party/wikisql

Step 2: Build and run the Docker image

We have provided a Dockerfile that sets up the entire environment for you. It assumes that you mount the datasets downloaded in Step 1 as a volume /mnt/data into a running image. Thus, the environment setup for RAT-SQL is:

docker build -t ratsql .
docker run --rm -v /path/to/data:/mnt/data -it ratsql

Within the image, add the location of WikiSQL scripts to PYTHONPATH so that their internal imports can be resolved by Python:

export PYTHONPATH=/app/third_party/wikisql/:$PYTHONPATH

If you prefer to set up and run the codebase without Docker, follow the steps in Dockerfile one by one. Note that this repository requires Python 3.7 or higher and a JVM to run Stanford CoreNLP.

Step 3: Run the experiments

Every experiment has its own config file in experiments. The pipeline of working with any model version or dataset is:

python run.py preprocess experiment_config_file  # Step 3a: preprocess the data
python run.py train experiment_config_file       # Step 3b: train a model
python run.py eval experiment_config_file        # Step 3b: evaluate the results

Use the following experiment config files to reproduce our results:

• Spider, GloVE version: experiments/spider-glove-run.jsonnet
• Spider, BERT version (requires a GPU with at least 16GB memory): experiments/spider-bert-run.jsonnet
• WikiSQL, GloVE version: experiments/wikisql-glove-run.jsonnet

The exact model accuracy may vary by ±2% depending on a random seed. See paper for details.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.