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MT-Teql-artifact

Artifact of our paper "MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations" (VLDB’22). The code is tested with Python 3.6. The dependencies can be installed with the following command.

pip install -r requirements.txt

Data Preparation

  1. download Spider dataset from https://yale-lily.github.io/spider
  2. place train_spider.json, dev.json and tables.json under data folder.

Augmented Dataset, Pretrained Model Weight File and Experimental Data

Please refer to http://bit.ly/MT-Teql-data. We released all data to reproduce our experiments.

Generate Mutations

Adjust the path in trans.py, and run the following command.

python trans.py

you may wish to download pre-generated synthetic data from our supplementary material and unzip mutation.zip to mutation/.

Test Models

In general, you need to adjust the data reader modules provided by different model implementations for testing models. We provided two sample test result of standard IRNet and augmented IRNet models in result/irnet-base.txt and result/irnet-as.txt for reproducing our result. You can run the following command.

python metamorphic_evaluation.py -t mutation/dev-tables.json -o result/irnet-base.txt

The script will output a list of metrics we used in our paper.

Model Augmentation

You can use the provided training data to further augment any models which is compatible to the standard Spider dataset. We also provide the augmented IRNet model (in model/irnet-as.model) and corresponding testing result (in result/irnet-as.txt) for comparison. You can follow the official instruction of IRNet to evaluate the augmented model.