Translating Natural Language Queries to SQL Using the T5 Model
This work addresses the need for automated SQL generation from natural language for companies, though it is incremental as it applies an existing model to specific database environments.
The paper tackled the problem of translating natural language queries to SQL using the T5 model, achieving exact match accuracies of 73% for an online transaction processing system and 84% for a data warehouse.
This paper presents the development process of a natural language to SQL model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, have a 73\% and 84\% exact match accuracy respectively. These models, in conjunction with other work completed in the research project, were implemented for several companies and used successfully on a daily basis. The approach used in the model development could be implemented in a similar fashion for other database environments and with a more powerful pre-trained language model.