This directory contains the code for seq2seq with attention-based copying from the input sequence.
Requirements:
- tensorflow==1.3.0 OR tensorflow-gpu==1.3.0
- nltk==3.2.5 and punkt tokenizer models
- numpy==1.13.1
- PyYAML==3.11
- scikit-learn==0.18.2
- scipy==0.19.0
- sqlparse==0.2.4
To run an example, starting from this directory:
export S2S_HOME=$(pwd)
./prep_advising.sh
python2 config_builder.py experimental_configs/example_config.yml
cd models/copy_input/advising_query_split/
./experiment.sh
Console output for the prep_advising script should look like the contents of sample_output/expected_prep_script_output.txt.
Console output for the config_builder should look like sample_output/expected_config_builder_output.txt.
The experiment will take several hours to run. Its output will be in models/copy_input/advising_query_split/quick_eval.txt (dev set) and models/copy_input/advising_query_split/quick_eval_train.txt (train set).
Examples of these output files are in sample_output/.
To run your own experiments, modify the prep_advising.sh to refer to the dataset
of your choice, and adjust the hyperparameters the config YAML file.
Note that the hyperparameters in example_config.yml are an example only.
Actual hyperparameters used in the paper are available here.
If you use this code, please cite our ACL paper:
@InProceedings{data-sql-advising,
author = {Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev},
title = {Improving Text-to-SQL Evaluation Methodology},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2018},
address = {Melbourne, Victoria, Australia},
pages = {351--360},
url = {http://aclweb.org/anthology/P18-1033},
}This code is built on top of tf-seq2seq, which is documented here and available here as of July 2018.