System, method, and computer program for augmenting multi-turn text-to-SQL datasets with self-play

An apparatus for augmenting multi-turn text-to-SQL datasets, the apparatus comprising:

• at least one memory configured to store computer program code;
• and
• at least one processor configured to read the computer program code and operate as instructed by the computer program code, the computer program code including:
• generating code configured to cause the at least one processor to generate an SQL-to-text model to converse with a text-to-SQL model;
• training code configured to cause the at least one processor to pre-train the SQL-to-text model and the text-to-SQL model based on input training data;
• first sampling code configured to cause the at least one processor to sample an SQL query as a goal query of an interaction between the SQL-to-text model and the text-to-SQL model;
• synthetic interaction generating code configured to cause the at least one processor to generate the interaction based on the goal query, a current utterance, previous utterances in the interaction, an SQL query from a preceding turn of the interaction, and a serialized database;
• filtering code configured to cause the at least one processor to filter the interaction based on a last turn of the interaction and the goal query, wherein the interaction is kept or filtered out based on a similarity score between the last turn of the interaction and the goal query;
• and
• utterance generating code configured to cause the at least one processor to concatenate the goal query, the SQL query from the preceding turn of the interaction, the previous utterances in the interaction, and a schema of the serialized database, and use a result of the concatenation to generate a next utterance in the interaction using the SQL-to-text model.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of an apparatus for augmenting multi-turn text-to-SQL datasets, cause the at least one processor to:

• generate an SQL-to-text model to converse with a text-to-SQL model;
• pre-train the SQL-to-text model and the text-to-SQL model based on input training data;
• sample an SQL query as a goal query of an interaction between the SQL-to-text model and the text-to-SQL model;
• generate the interaction based on the goal query, a current utterance, previous utterances in the interaction, an SQL query from a preceding turn of the interaction, and a serialized database;
• filter the interaction based on a last turn of the interaction and the goal query, wherein the interaction is kept or filtered out based on a similarity score between the last turn of the interaction and the goal query,
• wherein generating the interaction comprises concatenating the goal query, the SQL query from the preceding turn of the interaction, the previous utterances in the interaction, and a schema of the serialized database, and using a result of the concatenating as an input to the SQL-to-text model for generating a next utterance in the interaction using the SQL-to-text model.