Borealis AI is releasing a technical demo of Turing by Borealis AI, a state-of-the-art text to SQL database interface for non-technical users. The system translates natural language questions to SQL queries, giving non-technical users a new level of access to relational databases – a valuable opportunity to unlock new insights without having to write a single line of code.
In the technical demo, users get to see this interactive system at work.
The value proposition of a project like this is about democratizing data-driven insights by enabling non-technical users to interact with structured data, using natural language.
“Today, a lot of potentially useful knowledge and insights is trapped in databases, and only technical users can access that information, typically by using SQL. Turing by Borealis AI’s database interface unlocks these insights for non-technical users, who can query the multitude of databases using natural language and get the results and insights they need.”
– Yanshuai Cao, Senior Research Team Lead at Borealis AI
Turing by Borealis AI comes closer than most of technology available today, achieving and holding state-of-the-art performance levels, while reducing accuracy issues. Such cross-domain text-to-SQL semantic parsers generally have serious accuracy and usability problems, making practical applications a challenge. Unlike in online search, where approximate answers can be good enough, when users query relational databases to glean specific insights, high degree of accuracy is needed to provide value. With Turing by Borealis AI’s technology, a user can look at multiple hypotheses and with the help of explanation Turing by Borealis AI provides, can figure out which of the SQL queries comes closest to the search intent.
- SQL responses are explained in plain English to help with evaluating and understanding the results, which helps non-technical users select the appropriate SQL query from the highest-ranked options.
- Trained on 100+ databases, it can generalize to new, never-seen-before databases to answer NLP questions.
- Equipped with a state-of-the-art cross-domain semantic parser (we will be releasing the core of the semantic parser in August)
- Its text-to-SQL framework is evaluated on the Spider benchmark, placing among the top performing frameworks. Turing has achieved the record of the best performance in 2020 and held it for much of 2020/21.
Sample Use Case
Here’s a sample use case: Let’s say a non-technical user is in the business of delivering supplies to gas stations. The user wants to query available databases and find out which stations to contact next, in order to grow the business. How would the user get these business insights, without relying on SQL to do the search across available databases? With Turing by Borealis AI, users can start the search by picking the ‘gas station domain’ and ask: “What are the locations with gas stations owned by companies making over 100 billion in sales?” Under the hood, there is a deep learning model that treats the text-to-SQL problem as graph-to-tree mapping and produces a SQL query, executing it against the database to return the results.
Turing by Borealis AI generates SQL and uses a synchronous context-free grammar system to provide a high-precision explanation, so that users can make sure the results are trustworthy and match the intent.
Learn more about cross-database text-to-SQL in this blog, with further details on Turing by Borealis AI in this paper and arXiv:2106.04559.
What’s next for Turing by Borealis AI?
The team is presenting Turing by Borealis AI and related works: two main conference papers, one demo paper and one workshop paper at the joint conference of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) on August 1-6, 2021. The team is also aiming to release the core of its semantic parsing at that time.