Criteria2SQL

Dataset and source code for our work Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing.

Source code (src)

• The implementation based on SQLova.

Requirements

• python3.6 or higher.
• PyTorch 0.4.0 or higher.
• CUDA 9.0
• Python libraries: babel, matplotlib, defusedxml, tqdm
• Example
  • Install minicoda
  • conda install pytorch torchvision -c pytorch
  • conda install -c conda-forge records
  • conda install babel
  • conda install matplotlib
  • conda install defusedxml
  • conda install tqdm
• The code has been tested on GTX 1080 Ti running on Ubuntu 16.04.4 LTS.

Training and Testing

• To train the model by running: python train.py --seed 1 --bS 2 --accumulate_gradients 8 --bert_type_abb uS --fine_tune --lr 0.001 --lr_bert 0.00001 --max_seq_leng 512 on terminal.

• To test on pre-trained model by running: python test.py --seed 1 --bS 2 --accumulate_gradients 8 --bert_type_abb uS --max_seq_leng 512 on terminal.

• Pre-trained models can be download from here.

Dataset (data)

Our dataset follows same format as WikiSQL, while includes new types of SQL queries for order-sensitive eligibility criteria, counting-based eligibility criteria, boolean-type eligibility criteria.

Citation

If you use Criteria2SQL, please cite the following work:

@InProceedings{yu-EtAl:2020:LREC,
  author    = {Yu, Xiaojing  and  Chen, Tianlong  and  Yu, Zhengjie  and  Li, Huiyu  and  Yang, Yang  and  Jiang, Xiaoqian  and  Jiang, Anxiao},
  title     = {Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing},
  booktitle      = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month          = {May},
  year           = {2020},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  pages     = {5831--5839
  }