The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL"

Due to the size limitation, please download the dataset from Google Drive.

If you want to use TriageSQL in your work, please cite as follows:

@article{zhang2020did,
  title={Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL},
  author={Zhang, Yusen and Dong, Xiangyu and Chang, Shuaichen and Yu, Tao and Shi, Peng and Zhang, Rui},
  journal={arXiv preprint arXiv:2010.12634},
  year={2020}
}

In each json file of the dataset, one can find a field called type, which includes 5 different values, including small talk, answerable, ambiguous, lack data, and unanswerable by sql, corresponding to 5 different types described in our paper. Here is the summary of our dataset and the corresponding experiment results:

The folder src contains all the source files used to construct the proposed TriageSQL. In addition, some part of files contains more details about the dataset, such as databaseid which is the id of the schema in the original dataset, e.g. "flight_2" in CoSQL, while question_datasetid indicates the original dataset name of the questions, e.g. "quac". Some of the samples do not contain these fields because they are either human-annotated or edited.

We also include the source code for RoBERTa baseline in our project in /model. It is a multi-classifer with 5 classes where '0' represents answerable, '1'-'4' represent distinct types of unanswerable questions. Given the dataset from Google Drive, you may need to conduct some preprocessing to obtain train/dev/test set. You can directly download from here or make your own dataset using the following instructions:

The same as /testset/test.json, our input file is a json list with shape (num_of_question, 3) containing 3 lists: query, schema, and label.

• query: containing strings of questions
• schema: contianing strings of schema for each question, i.e., "table_name.column_name1 | table_name.column_name2 | ... " for multi-table questions, and column_name1 | column_name2 for single-table questions.
• labels of questions, see config.label_dict for the mapping, leave arbitary value if testing is not needed or true labels are not given.

when preprocessing, please use lower case for all data, and remove the meaningless table names as well, such as T10023-1242. Also, we sample 10k from each type to form the large input dataset

After adjusting the parameters in config.py, one can simply run python train.py or python eval.py to train or evaluate the model.

• config.py: hyper parameters
• train.py: training and evaluation of the model
• utils.py: loading the dataset and tokenization
• model.py: the RoBERTa classification model we used
• test.json: sample of test input