Managing ambiguous date mentions in transforming natural language to a logical form

A system comprising:

• one or more processors;
• and
• one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising:
• accessing training data comprising a set of training examples, wherein each training example of the set of training examples comprises a natural language utterance, a logical form corresponding to the natural language utterance, and database schema information, wherein the database schema information comprises a date-type attribute;
• generating augmented training data comprising a set of augmented training examples and the set of training examples, wherein each augmented training example of the set of augmented training examples corresponds to at least one training example of the set of training examples, and generating each augmented training example of the set of augmented training examples comprises:
• modifying a particular natural language utterance of a particular training example of the set of training examples,
• modifying a particular logical form of the particular training example, and
• modifying particular database schema information of the particular training example, wherein modifying the particular database schema information comprises adding an additional date-type attribute to the particular database schema information;
• training a machine learning model with the augmented training data to generate a trained machine learning model;
• and
• providing the trained machine learning model.

One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause a system to perform operations comprising:

• accessing training data comprising a set of training examples, wherein each training example of the set of training examples comprises a natural language utterance, a logical form corresponding to the natural language utterance, and database schema information, wherein the database schema information comprises a date-type attribute;
• generating augmented training data comprising a set of augmented training examples and the set of training examples, wherein each augmented training example of the set of augmented training examples corresponds to at least one training example of the set of training examples, and generating each augmented training example of the set of augmented training examples comprises:
• modifying a particular natural language utterance of a particular training example of the set of training examples,
• modifying a particular logical form of the particular training example, and
• modifying particular database schema information of the particular training example, wherein modifying the particular database schema information comprises adding an additional date-type attribute to the particular database schema information;
• training a machine learning model with the augmented training data to generate a trained machine learning model;
• and
• providing the trained machine learning model.