Processing Queries using an Attention-Based Ranking System

A computing system for ranking a plurality of candidate result items in response to a query, the query comprising one or more tokens, and each candidate result item comprising one or more tokens, the computing system comprising:

• hardware logic circuitry that implements a first attention-based neural network, a second attention-based neural network, and a scoring neural network,
• the first attention-based neural network being configured to generate a plurality of instances of original query-to-result similarity information respectively corresponding to the plurality of candidate result items, each instance of original query-to-result similarity information expressing an extent of attention that each token of the query should pay to each token of a particular candidate result item;
• the first attention-based neural network being configured to use a set of original features to describe the query and the plurality of candidate result items,
• the plurality of instances of original query-to-result similarity information being combined with supplemental feature information pertaining to the query, to generate a plurality of respective instances of enhanced similarity information;
• the second attention-based neural network being configured to generate a plurality of instances of result-to-result context information using self-attention, each instance of result-to-result context information expressing an extent of attention that a particular instance of enhanced similarity information should pay to other instances of enhanced similarity information;
• and
• the scoring neural network being configured to map the plurality of instances of result-to-result context information into plural respective ranking scores, the plural ranking scores identifying an order of relevance of the plurality of candidate result items to the query.

A computer-readable storage medium for storing computer-readable instructions, the computer-readable instructions, when executed by one or more hardware processors, performing a method that comprises:

• obtaining a query and a plurality of candidate result items that match the query, the query comprising one or more tokens, and each candidate result item comprising one or more tokens;
• generating, using a first attention-based neural network implemented by the computer-readable instructions, a plurality of instances of query-to-result similarity information respectively corresponding to the plurality of candidate result items, each instance of query-to-result similarity information expressing an extent of attention that each token of the query should pay to each token of a particular candidate result item,
• wherein the first attention-based neural network generates preliminary output results, and wherein the first attention-based neural network generates the plurality of instances of query-to-result similarity information by generating an element-by-element difference of the preliminary output results and one or more instances of query embedding information produced from the query;
• and
• generating plural ranking scores based on the plurality of instances of query-to-result information, the plural ranking scores identifying an order of relevance of the plurality of candidate result items to the query.