Query interpreter training with adversarial table perturbations

A method performed by a computing system for processing natural language queries to structured language queries for a database system, the method comprising:

• at a query interpreter:
• receiving a subject natural language query for the database system;
• translating the subject natural language query to a structure language query;
• and
• providing the structured language query to a data retrieval engine of the database system;
• wherein the query interpreter was previously trained using one or more adversarial training examples that were generated by a computer-implemented table augmentation framework that:
• obtains a target data table for a natural language query;
• identifies a primary entity of the target data table;
• for a target domain of the target data table, generates a set of candidate identifiers that are each semantically associated with an identifier of the target domain;
• for each candidate identifier, provides a premise-hypothesis pair to a natural language inference (NLI) model to generate an entailment score in which:
• the premise of the premise-hypothesis pair includes an identifier of the primary entity and the identifier of the target domain, and
• the hypothesis of the premise-hypothesis pair includes the identifier of the primary entity and the candidate identifier;
• selects a first subset of candidate identifiers from among the set of candidate identifiers based on the entailment score generated for each premise-hypothesis pair;
• for each candidate identifier of the first subset, applies the candidate identifier to an instance of the target data table as a table perturbation to generate a perturbed data table;
• and
• outputs each perturbed data table as part of an adversarial training example of the set of one or more adversarial training examples.

A computing system, comprising:

• a storage machine having instructions stored thereon executable by a logic machine to:
• generate a set of one or more adversarial training examples for training a computer-implemented natural language-to-structured language query interpreter of a database system by:
• obtaining a target data table for a natural language query;
• identifying a primary entity of the target data table;
• for a target domain of the target data table, generating a set of candidate identifiers that are each semantically associated with an identifier of the target domain;
• for each candidate identifier, providing a premise-hypothesis pair to a natural language inference (NLI) model to generate an entailment score in which:
• the premise of the premise-hypothesis pair includes an identifier of the primary entity and the identifier of the target domain, and
• the hypothesis of the premise-hypothesis pair includes the identifier of the primary entity and the candidate identifier;
• selecting a first subset of candidate identifiers from among the set of candidate identifiers based on the entailment score generated for each premise-hypothesis pair;
• for each candidate identifier of the first subset, applying the candidate identifier to an instance of the target data table as a table perturbation to generate a perturbed data table;
• and
• outputting each perturbed data table as part of an adversarial training example of the set of one or more adversarial training examples.