Cross-Example Softmax for Embedding Space Calibration
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Solution Overview
Problem
Conventional information retrieval systems using neural network models struggle with calibrating distances across queries, making it difficult to determine the relevance of documents based on absolute distances, as these distances are not comparable and lack interpretability.
Innovation Solution
The implementation of cross-example Softmax methods and cross-example negative mining techniques to learn embedding spaces, where distances between queries and documents are globally calibrated, ensuring that matching pairs are closer than non-matching pairs, and negative examples are selected from across the entire batch for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Softmax or stochastic negative mining methods are used to train embedding spaces, then the training process is computationally efficient and simple, but the distances between queries and documents are not globally calibrated and cannot be compared across different queries
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) initial embedding space learning using conventional efficient methods, and (2) subsequent global calibration using cross-example Softmax and cross-example negative mining. This segmentation allows the system to first obtain a functional embedding space quickly, then progressively improve distance calibration without sacrificing the initial training efficiency.
Solution Approach 2:
The patent performs preliminary action by first training the embedding space using computationally efficient conventional methods before applying the more complex cross-example calibration techniques. This preliminary training establishes a baseline embedding space that can be subsequently refined, avoiding the need to perform complex calculations from scratch.
2Reliability
If conventional embedding space learning methods are used, then training computational cost is reduced, but the relevance interpretation of distances becomes unreliable and incomparable across queries
Solution Approach 1:
The patent implements periodic action by alternating between conventional training steps (for efficiency) and cross-example calibration steps (for reliability). The cross-example Softmax and negative mining operations are performed periodically during training to enforce global distance calibration, ensuring that distance values remain interpretable as relevance metrics while maintaining reasonable training efficiency through the periodic rather than continuous application of calibration constraints.
3Measurement precision
If cross-example Softmax and cross-example negative mining are used to learn embedding spaces, then distances are globally calibrated and comparable across queries, but the training process becomes more complex and computationally intensive
Solution Approach 1:
The patent applies partial action by implementing cross-example calibration selectively rather than exhaustively. The cross-example Softmax and negative mining operations are applied to a sampled subset of training examples rather than all possible pairs, and the calibration is performed at specific training intervals rather than continuously. This partial application maintains distance comparability while reducing the computational burden to manageable levels.
Data Source
AI summary
Techniques are disclosed that enable learning an embedding space using cross-examples, where a distance between a query and an electronic resource in the embedding space provides an indication of the relevance of the electronic resource to the query. Various implementations include learning the embedding space using cross-example Softmax techniques. Various implementations include leaning the embedding space using cross-example negative mining. Additional or alternative techniques are disclosed that enable determining an electronic resource for a query based on comparing a query vector (e.g., a embedding space representation of the query) with a set of pre-stored candidate electronic resource vectors (e.g., an embedding space representation of a set of candidate electronic resources).