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SQLova_model

README.md

SQLova Experiments

1. Description

This folder contains implementation of interactive SQLova parser, which uses SQLova as a base semantic parser in our MISP framework:

The implementation is adapted from the SQLova repository. Please cite the following papers if you use the code:

@inproceedings{yao2020imitation,
  title={An Imitation Game for Learning Semantic Parsers from User Interaction},
  author={Yao, Ziyu and Tang, Yiqi and Yih, Wen-tau and Sun, Huan and Su, Yu},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2020}
}

@inproceedings{yao2019model,
  title={Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study},
  author={Yao, Ziyu and Su, Yu and Sun, Huan and Yih, Wen-tau},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={5450--5461},
  year={2019}
}

@article{hwang2019comprehensive,
  title={A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization},
  author={Hwang, Wonseok and Yim, Jinyeung and Park, Seunghyun and Seo, Minjoon},
  journal={arXiv preprint arXiv:1902.01069},
  year={2019}
}

2. General Environment Setup

Environment

  • Please install the Anaconda environment from gpu-py3.yml:

    conda env create -f gpu-py3.yml

  • Download Pretrained BERT model from here as SQLova_model/download/bert/pytorch_model_uncased_L-12_H-768_A-12.bin.

Data

We have the pre-processed WikiSQL data available: data.tar. Please download and uncompress it via tar -xvf data.tar as a folder SQLova_model/download/data.

If you would like to preprocess the WikiSQL data (or your own data) from scratch, please follow the data_preprocess.sh script.

3. MISP with SQLova (EMNLP'19)

3.1 Model training

To train SQLova on the full training set, please revise SETTING=full_train in scripts/sqlova/pretrain.sh. In the main directory, run:

bash scripts/sqlova/pretrain.sh

3.2 Model testing without interaction

To test SQLova regularly, in scripts/sqlova/test.sh, please revise SETTING to ensure that the model checkpoint is loaded from the desired MODEL_DIR folder and revise TEST_JOB for testing on WikiSQL dev/test set. In the main directory, run:

bash scripts/sqlova/test.sh

3.3 Model testing with simulated user interaction

To test SQLova with human interaction under the MISP framework, in scripts/sqlova/test_with_interaction.sh, revise SETTING to ensure that the model checkpoint is loaded from the desired MODEL_DIR folder and revise DATA for testing on WikiSQL dev/test set. In the main directory, run:

bash scripts/sqlova/test_with_interaction.sh

4. Learning SQLova from user interaction (EMNLP'20)

Throughout the experiments, we consider three initialization settings:

  • SETTING=online_pretrain_1p for using 1% of full training data for initialization;
  • SETTING=online_pretrain_5p for using 5% of full training data for initialization;
  • SETTING=online_pretrain_10p for using 10% of full training data for initialization.

Please revise the SETTING variable in each script accordingly.

4.1 Pretraining

4.1.1 Pretrain by yourself

Before interactive learning, we pretrain the SQLova parser with a small subset of the full training data. Please revise SETTING in scripts/sqlova/pretrain.sh accordingly for different initialization settings. Then in the main directory, run:

bash scripts/sqlova/pretrain.sh

4.1.2 Use our pretrained checkpoints

You can also use our pretrained checkpoints: initialization_checkpoints_folder.tar. Please download and uncompress the folder via tar -xvf initialization_checkpoints_folder.tar and place the content as:

|- SQLova_model
|   |-- checkpoints_onlint_pretrain_1p
|       |-- model_best.pt
|       |-- model_bert_best.pt
|   |-- checkpoints_onlint_pretrain_5p
|   |-- checkpoints_onlint_pretrain_10p

4.1.3 Test the pretrained models

To test the pretrained parser without user interaction, see 3.2 Model testing without interaction. To test the pretrained parser with simulated user interaction, see 3.3 Model testing with simulated user interaction. Make sure the SETTING variable is set correctly.

4.2 Interactive learning

The training script for each algorithm can be found below. Please run them in the main directory and remember to set SETTING accordingly for different initialization settings.

Algorithm Script
MISP_NEIL scripts/sqlova/misp_neil.sh
Full Expert scripts/sqlova/full_expert.sh
Binary User scripts/sqlova/bin_user.sh
Binary User+Expert scripts/sqlova/bin_user_expert.sh
Self Train scripts/sqlova/self_train_0.5.sh
MISP_NEIL* scripts/sqlova/misp_neil_perfect.sh