Editing-based SQL Query Generation for Cross-Domain Context-Dependent Questions

Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher and Dragomir Radev

Abstract
We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.

@inproceedings{zhang-etal-2019-editing,
    title = "Editing-Based {SQL} Query Generation for Cross-Domain Context-Dependent Questions",
    author = "Zhang, Rui  and
    Yu, Tao  and
    Er, Heyang  and
    Shim, Sungrok  and
    Xue, Eric  and
    Lin, Xi Victoria  and
    Shi, Tianze  and
    Xiong, Caiming  and
    Socher, Richard  and
    Radev, Dragomir",
    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)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1537",
    doi = "10.18653/v1/D19-1537",
    pages = "5341--5352",
}