S²SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers

The PyTorch implementation of paper S²SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers （ACL 2022 Findings)

Please star this repo and cite paper if you want to use it in your work.

Step 1: Env Setup

Our experimental environment is consistent with LGESQL, so we can basically follow their settings.

Firstly, create conda environment text2sql: In our experiments, we use torch==1.6.0 and dgl==0.5.3 with CUDA version 10.1

We use NVIDIA V100-32GB for all experiments

conda create -n text2sql python=3.6
source activate text2sql
pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Next, download dependencies:

 python -c "import stanza; stanza.download('en')"
 python -c "import nltk; nltk.download('stopwords')"

Download electra-large-discriminator from Hugging Face Model Hub, into the pretrained_models directory.

 mkdir -p pretrained_models && cd pretrained_models
 git lfs install
 git clone https://huggingface.co/google/electra-large-discriminator

Step 2: Data Preparation

Download, unzip and rename the spider.zip into the directory data.

Merge the data/train_spider.json and data/train_others.json into one single dataset data/train.json.

Preprocess the train and dev dataset, including input normalization, schema linking, graph construction and output actions generation.

 ./run/run_preprocessing.sh

Step 3: Training and Evaluation

Training and eval S²SQL models with ELECTRA:

#msde: mixed static and dynamic embeddings
#mmc: multi-head multi-view concatenation
./run/run_lgesql_plm.sh [mmc|msde] electra-large-discriminator
./run/run_evaluation.sh 

Acknowledgements

We would like to thank Tao Yu, Yusen Zhang for running evaluations on our submitted models.

We are also grateful to LGESQL and RATSQL that inspires our works.