Search Results for author:
Found 5 papers, 4 papers with code
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment
To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance.
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization
Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting.
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.
Towards Robustness of Text-to-SQL Models against Synonym Substitution
We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks.
A Review of Cross-Domain Text-to-SQL Models
WikiSQL and Spider, the large-scale cross-domain text-to-SQL datasets, have attracted much attention from the research community.
