This is PointSQL, the source codes of Natural Language to Structured Query Generation via Meta-Learning and Pointing Out SQL Queries From Text from Microsoft Research. We present the setup for the WikiSQL experiments.

• Download a preprocessed dataset link to input/
• Untar the file tar -xvjf input.tar.bz2

1. Download data from WikiSQL.

$ cd wikisql_data
$ wget https://github.com/salesforce/WikiSQL/raw/master/data.tar.bz2
$ tar -xvjf data.tar.bz2

2. Put the lib directory under wikisql_data/scripts/
3. Run annotation using Stanza and preproces the dataset

$ cd wikisql_data/scripts/
$ python annotate.py
$ python prepare.py

4. Put the train/dev/test data into input/data for model training/testing.
5. Use relevance function to prepare relevance files and put them under input/nl2prog_input_support_rank

python wikisql_data/scripts/relevance.py

6. Download pretrained embeddings from glove and character n-gram embeddings and put them under input/

• A preprocessed dataset can be found here, where the wikisql_train.dat, wikisql_test.dat, wikisql_dev.dat are the files that can be directly used in training.

Note: the version 2 dataset matches the v1.1 release of WikiSQL. The preprocessing script wikisql_data/scripts/prepare_v2.py (python3 required) processes WikiSQL v1.1 raw data and table files to generate wikisql_train.dat, wikisql_test.dat, wikisql_dev.dat.

Meta + Sum loss training

$ OUTDIR=output/meta_sum
$ mkdir $OUTDIR
$ python run.py --input-dir ./input \
 --output-dir $OUTDIR \
 --config config/nl2prog.meta_2_0.001.rank.config \
 --meta_learning_rate 0.001 --gradient_clip_norm 5 \
 --num_layers 3 --num_meta_example 2 \
 --meta_learning --production

• Due to the preprocessing error, we ignore some development (see input/data/wikisql_err_dev.dat) and test (see input/data/wikisql_err_test.dat) set examples, we treat them as incorrect directly.

• Run evaluation as follows (replace model_zoo/meta_sum/table_nl_prog-40 with $OUTDIR/table_nl_prog-?? with the last checkpoint in the folder):

• Development set

$ mkdir -p ${OUTDIR}_dev
$ python run.py --input-dir ./input --output-dir ${OUTDIR}_dev \ 
 --config config/nl2prog.meta_2_0.001.rank.devconfig \
 --meta_learning --test-model model_zoo/meta_sum/table_nl_prog-40 --production

• Run execution for developement set as follows:

  $ cp ${OUTDIR}_dev/test_top_1.log dev_top_1.log
  $ python2 execute_dev.py 
   #Q2 (predition) result is wrong: 1254
   #Q1 or Q2 fail to parse: 0
   #Q1 (ground truth) exec to None: 20
   #Q1 (ground truth) failed to execute: 0
   Logical Form Accuracy: 0.631383269546
   Execute Accuracy: 0.68277747403
  

• Test set

$ mkdir -p ${OUTDIR}_test
$ python run.py --input-dir ./input --output-dir ${OUTDIR}_test \ 
 --config config/nl2prog.meta_2_0.001.rank.testconfig \
 --meta_learning --test-model model_zoo/meta_sum/table_nl_prog-40 --production

• Run execution for test set as follows:

  $ cp ${OUTDIR}_test/test_top_1.log .
  $ python2 execute.py
   #Q2 (predition) result is wrong: 2556
   #Q1 or Q2 fail to parse: 0
   #Q1 (ground truth) exec to None: 48
   #Q1 (ground truth) failed to execute: 0
   Logical Form Accuracy: 0.628073829775
   Execute Accuracy: 0.680379563733
  

• Baseline model on test set

$ OUTDIR=output/base_sum
$ python run.py --input-dir ./input --output-dir ${OUTDIR}_test \
 --config config/nl2prog.testconfig --production \
 --test-model model_zoo/base_sum/table_nl_prog-79 --production

• Run execution for the baseline model on test set as follows:

  $ cp ${OUTDIR}_test/test_top_1.log .
  $ python2 execute.py
   #Q2 (predition) result is wrong: 2636
   #Q1 or Q2 fail to parse: 0
   #Q1 (ground truth) exec to None: 48
   #Q1 (ground truth) failed to execute: 0
   Logical Form Accuracy: 0.614592374009
   Execute Accuracy: 0.668055314471
  

• Download pretrained model checkpoints to model_zoo/

• Run tar -xvjf model_zoo.tar.bz2 to extract pretrain models.

  • Meta + Sum loss: model_zoo/meta_sum
  • Base Sum loss: model_zoo/base_sum

• Tensorflow 1.4
• python 3.6
• Stanza

If you use the code in your paper, then please cite it as:

@inproceedings{pshuang2018PT-MAML,
 author = {Po{-}Sen Huang and
 Chenglong Wang and
 Rishabh Singh and
 Wen-tau Yih and
 Xiaodong He},
 title = {Natural Language to Structured Query Generation via Meta-Learning},
 booktitle = {NAACL},
 year = {2018},
}

@inproceedings{2018executionguided,
 author = {Chenglong Wang and
 Po{-}Sen Huang and
 Alex Polozov and
 Marc Brockschmidt and 
 Rishabh Singh},
 title = "{Execution-Guided Neural Program Decoding}",
 booktitle = {ICML workshop on Neural Abstract Machines & Program Induction v2 (NAMPI)},
 year = {2018}
}

and

@techreport{chenglong,
 author = {Wang, Chenglong and Brockschmidt, Marc and Singh, Rishabh},
 title = {Pointing Out {SQL} Queries From Text},
 number = {MSR-TR-2017-45},
 year = {2017},
 month = {November},
 url = {https://www.microsoft.com/en-us/research/publication/pointing-sql-queries-text/},
}

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.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.