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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.36</identifier>
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%0 Conference Proceedings
%T Conversing with databases: Practical Natural Language Querying
%A Kochedykov, Denis
%A Yin, Fenglin
%A Khatravath, Sreevidya
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kochedykov-etal-2023-conversing
%X 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.
%R 10.18653/v1/2023.emnlp-industry.36
%U https://aclanthology.org/2023.emnlp-industry.36
%U https://doi.org/10.18653/v1/2023.emnlp-industry.36
%P 372-379

Markdown (Informal)

[Conversing with databases: Practical Natural Language Querying](https://aclanthology.org/2023.emnlp-industry.36) (Kochedykov et al., EMNLP 2023)

• Conversing with databases: Practical Natural Language Querying (Kochedykov et al., EMNLP 2023)

ACL

• Denis Kochedykov, Fenglin Yin, and Sreevidya Khatravath. 2023. Conversing with databases: Practical Natural Language Querying. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 372–379, Singapore. Association for Computational Linguistics.