@inproceedings{kochedykov-etal-2023-conversing,
    title = "Conversing with databases: Practical Natural Language Querying",
    author = "Kochedykov, Denis  and
      Yin, Fenglin  and
      Khatravath, Sreevidya",
    editor = "Wang, Mingxuan  and
      Zitouni, Imed",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-industry.36/",
    doi = "10.18653/v1/2023.emnlp-industry.36",
    pages = "372--379",
    abstract = "In this work, we designed, developed and released in production DataQue {--} a hybrid NLQ (Natural Language Querying) system for conversational DB querying. We address multiple practical problems that are not accounted for in public Text-to-SQL solutions {--} numerous complex implied conditions in user questions, jargon and abbreviations, custom calculations, non-SQL operations, a need to inject all those into pipeline fast and to have guaranteed parsing results for demanding users, cold-start problem. The DataQue processing pipeline for Text-to-SQL translation consists of 10-15 model-based and rule-based components that allows to tightly control the processing."
}