( Image credit: SyntaxSQLNet )
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Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks.
As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results.
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequence model to directly generate the SQL query for a given question, and further performs the required edits using an attentive-copying mechanism and task-specific look-up tables.
We present a simple way to do the task of text-to-SQL problem with weak supervision.
When translating natural language questions into SQL queries to answer questions from a database, we would like our methods to generalize to domains and database schemas outside of the training set.
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.