|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.
SOTA for Text-To-Sql on WikiSQL
A significant amount of the world's knowledge is stored in relational databases.
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
SOTA for Semantic Parsing on spider
Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work.
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
We focus on the cross-domain context-dependent text-to-SQL generation task.
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.