Methods and systems for controlled modeling and optimization of a natural language database interface

A computer-implemented method for controlled generation of a corpus of optimized datasets for training a machine learning-based model of a natural language interface to a database, the method comprising:

• (a) receiving insights data regarding the database from a database insights engine, and based at least in part on the database insights data automatically generating a plurality of subject seed queries, each of the plurality of subject seed queries mapping a subject in a logical domain to a physical model of a schema of the database;
• (b) generating a plurality of database queries by performing semantic multiplication of the plurality of subject seed queries;
• (c) generating a data distribution of natural language queries paired with corresponding database queries by, for each one of the plurality of database queries, pairing the database query to a natural language query and one or more paraphrases of the natural language query;
• (d) projecting the data distribution onto a plurality of segmented text distributions and applying one or more control signals to automatically determine an optimal training data distribution, wherein the control signals are provided from one or more of an external knowledge database, a training data balancer, a model training system, and a failure analysis system;
• and
• (e) transforming the optimal training data distribution into pairs of natural language queries and corresponding database queries to generate the corpus of optimized training datasets.