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.- Anthology ID:
- 2023.emnlp-industry.36
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 372–379
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-industry.36
- DOI:
- 10.18653/v1/2023.emnlp-industry.36
- Cite (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.
- Cite (Informal):
- Conversing with databases: Practical Natural Language Querying (Kochedykov et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-industry.36.pdf