@inproceedings{wang-etal-2020-pg,
    title = "{PG}-{GSQL}: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-{SQL} Generation",
    author = "Wang, Huajie  and
      Li, Mei  and
      Chen, Lei",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.coling-main.33/",
    doi = "10.18653/v1/2020.coling-main.33",
    pages = "370--380",
    abstract = "Text-to-SQL is a task of translating utterances to SQL queries, and most existing neural approaches of text-to-SQL focus on the cross-domain context-independent generation task. We pay close attention to the cross-domain context-dependent text-to-SQL generation task, which requires a model to depend on the interaction history and current utterance to generate SQL query. In this paper, we present an encoder-decoder model called PG-GSQL based on the interaction-level encoder and with two effective innovations in decoder to solve cross-domain context-dependent text-to-SQL task. 1) To effectively capture historical information of SQL query and reuse the previous SQL query tokens, we use a hybrid pointer-generator network as decoder to copy tokens from the previous SQL query via pointer, the generator part is utilized to generate new tokens. 2) We propose a guide component to limit the prediction space of vocabulary for avoiding table-column dependency and foreign key dependency errors during decoding phase. In addition, we design a column-table linking mechanism to improve the prediction accuracy of tables. On the challenging cross-domain context-dependent text-to-SQL benchmark SParC, PG-GSQL achieves 34.0{\%} question matching accuracy and 19.0{\%} interaction matching accuracy on the dev set. With BERT augmentation, PG-GSQL obtains 53.1{\%} question matching accuracy and 34.7{\%} interaction matching accuracy on the dev set, outperforms the previous state-of-the-art model by 5.9{\%} question matching accuracy and 5.2{\%} interaction matching accuracy. Our code is publicly available."
}