@inproceedings{lin-etal-2020-bridging,
    title = "Bridging Textual and Tabular Data for Cross-Domain Text-to-{SQL} Semantic Parsing",
    author = "Lin, Xi Victoria  and
      Socher, Richard  and
      Xiong, Caiming",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.findings-emnlp.438/",
    doi = "10.18653/v1/2020.findings-emnlp.438",
    pages = "4870--4888",
    abstract = "We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the question. The hybrid sequence is encoded by BERT with minimal subsequent layers and the text-DB contextualization is realized via the fine-tuned deep attention in BERT. Combined with a pointer-generator decoder with schema-consistency driven search space pruning, BRIDGE attained state-of-the-art performance on the well-studied Spider benchmark (65.5{\%} dev, 59.2{\%} test), despite being much simpler than most recently proposed models for this task. Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks. Our model implementation is available at \url{https://github.com/salesforce/TabularSemanticParsing}."
}