LGESQL

This is the project containing source code for the paper LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations in ACL 2021 main conference. If you find it useful, please cite our work.

    @inproceedings{cao-etal-2021-lgesql,
            title = "{LGESQL}: Line Graph Enhanced Text-to-{SQL} Model with Mixed Local and Non-Local Relations",
            author = "Cao, Ruisheng  and
            Chen, Lu  and
            Chen, Zhi  and
            Zhao, Yanbin  and
            Zhu, Su  and
            Yu, Kai",
            booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
            month = aug,
            year = "2021",
            address = "Online",
            publisher = "Association for Computational Linguistics",
            url = "https://aclanthology.org/2021.acl-long.198",
            doi = "10.18653/v1/2021.acl-long.198",
            pages = "2541--2555",
    }

Create environment and download dependencies

1. Firstly, create conda environment lgesql:

• In our experiments, we use torch==1.7.1 and dgl-cu110==0.6.1 with CUDA version 11.0

  conda create -n lgesql python=3.7.9
  source activate lgesql
  conda install -c msys2 m2-base 
  conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch
  python -c "import torch;print(torch.cuda.is_available())"
  

2. Next, download source code and dependencies:

    git clone git@github.com:kanseaveg/lgesql-windows.git lgesql
    cd D:\DMIR\code\python\lgesql
    pip install -r requirements.txt
   
    python -c "import stanza; stanza.download('en')"
    python -c "import os; os.environ['HOME']=os.environ['HOMEPATH'];from embeddings import GloveEmbedding; emb = GloveEmbedding('common_crawl_48', d_emb=300)"
    python -c "import nltk; nltk.download('stopwords')"
    python -c "import nltk; nltk.download('punkt')"
   

3. Download pre-trained language models from Hugging Face Model Hub, such as bert-large-whole-word-masking and electra-large-discriminator, into the pretrained_models directory. The vocab file for glove.42B.300d is also pulled: (please ensure that Git LFS is installed)

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

4. Download the glove tools from here and move to the corresponding directory.

    mkdir -p glove.42b.300d && cd glove.42b.300d
    unzip glove.42b.300d.zip
    awk -v FS=' ' '{print $1}' glove.42B.300d.txt > vocab_glove.txt
   

Download and preprocess dataset

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

2. Merge the data/train_spider.json and data/train_others.json into one single dataset data/train.json. or you can download the data/train.json directly from here.

3. Preprocess the train and dev dataset, including input normalization, schema linking, graph construction and output actions generation. (Our preprocessed dataset can be downloaded here)

    .\run\run_preprocessing.bat
   

Training

Training LGESQL models with GLOVE, BERT and ELECTRA respectively:

• msde: mixed static and dynamic embeddings

• mmc: multi-head multi-view concatenation

  .\run\windows\run_lgesql_glove.bat [mmc|msde]
  .\run\windows\run_lgesql_plm.bat [mmc|msde] [bert-large-uncased-whole-word-masking|electra-large-discriminator]
  

Evaluation and submission

1. Create the directory saved_models, save the trained model and its configuration (at least containing model.bin and params.json) into a new directory under saved_models, e.g. saved_models/electra-mmc-66.4/. The model.bin and params.json shows in the training log, please check the log to get it.

2. For evaluation, see .\run\windows\run_evaluation.bat and .\run\windows\run_submission.bat (eval from scratch) for reference.

3. Model instances and submission scripts are available in codalab:plm and google drive: including submitted BERT and ELECTRA models. Codes and model for GLOVE are deprecated.

Results

Dev and test EXACT MATCH ACC in the official leaderboard, also provided in the results directory:

model	dev acc	test acc
LGESQL + GLOVE	67.6	62.8
LGESQL + BERT	74.1	68.3
LGESQL + ELECTRA	75.1	72.0

For Chinese Noob

You can lookup the Windows-LGESQL-Glove-Tutorial.pdf on the resources directory.

Acknowledgements

We would like to thank Tao Yu, Yusen Zhang and Bo Pang for running evaluations on our submitted models. We are also grateful to the flexible semantic parser TranX that inspires our works. Also, for this fork respository, thanks to DMIRLAB.