Generating and utilizing classification and query-specific models to generate digital responses to queries from client device

A system comprising:

• at least one processor;
• and
• a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
• identify a training query that corresponds to a first product specification of a training product, wherein the training product corresponds to a plurality of training product specifications;
• train a neural ranking model comprising a bidirectional long short-term memory layer and a differential product concatenation layer to generate relevance scores between product specifications and digital queries by, for each training product specification of the plurality of training product specifications:
• generating a training concatenated vector utilizing the differential product concatenation layer to concatenate a difference vector representing a difference between a training query vector and a training product specification vector together with a product vector representing a product of the training query vector and the training product specification vector;
• generating a relevance score indicating a measure of correspondence between the training product specification and the training query by utilizing the bidirectional long short-term memory layer to process the training concatenated vector;
• and
• comparing the relevance score to a ground truth score for the training product specification, the ground truth score indicating whether the training product specification is the first product specification that corresponds to the training query.