Photon: A Robust Cross-Domain Text-to-SQL System
By Jichuan Zeng, Xi Victoria Lin, Steven C. H. Hoi, Richard Socher, Caiming Xiong, Michael R. Lyu, Irwin King. 2020
58 citations recorded by the source corpus.
Natural language interfaces to databases(NLIDB) democratize end user access to relational data. Due to fundamental differences between natural language communication and programming, it is common for end users to issue questions that are ambiguous to the system or fall outside the semantic scope of its underlying query language. We present PHOTON, a robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined. PHOTON consists of a strong neural semantic parser (63.2% structure accuracy on the Spider dev benchmark), a human-in-the-loop question corrector, a SQL executor and a response generator. The question corrector isa discriminative neural sequence editor which detects confusion span(s) in the input question and suggests rephrasing until a translatable input is given by the user or a maximum number of iterations are conducted. Experiments on simulated data show that the proposed method effectively improves the robustness of text-to-SQL system against untranslatable user input.The live demo of our system is available at http://www.naturalsql.com
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Natural Language Processing
Citations in the corpus
- MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, methodology): “, 2021) for evaluating interpretable models, Photon (Zeng et al., 2020) for text-to-SQL tasks, and Huggingface’s”