Query correction based on reattempts learning

A method comprising:

• receiving a first input query and a second input query;
• accessing a set of reattempt query pairs stored at a data structure, wherein each respective pair in the set:
• comprises an initial query and a reattempt query;
• and
• is associated with an indication of whether a reply generated for output based on the respective query pair was acceptable;
• inputting, into a trained reattempt machine learning model, data indicative of the first input query and the second input query;
• receiving, from the trained reattempt machine learning model, an output probability indicating the second input query constitutes a reattempt query of the first input query;
• based at least in part on the output probability indicating that the second input query constitutes the reattempt query of the first input query, accessing the data structure to select a query pair from the set of reattempt query pairs based at least in part on receiving determinations that:
• (a) the query pair is related to at least one of the first input query or the second input query, and
• (b) the query pair is associated with an indication that a reply generated for output based on the query pair was acceptable;
• performing a search based on the selected query pair in the set of the reattempt query pairs;
• and
• generating for output a reply based on the performed search.