Gated Self-Attention Memory Network for Query Answer Matching
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Solution Overview
Problem
Conventional systems for answer selection in dialogue systems suffer from decreased accuracy and flexibility due to separate encoding of query and candidate answers, limited unit size analysis, and insufficient attention mechanisms, leading to unaccounted context and nuances.
Innovation Solution
The implementation of a gated self-attention mechanism within a memory network architecture that conditions a gate vector on a sequence of inputs, allowing for combined vector representations of queries and candidate answers, and accounting for contextual information to enhance matching accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate encoding of query and candidate answers is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent merges the encoding of query and candidate answers into a unified attention mechanism. The attention mechanism processes both query and candidate answers together through shared weight matrices and attention scores, creating a combined representation that captures their relationships. This merging improves matching accuracy by considering interactions between query and candidate answer tokens simultaneously, rather than encoding them separately and then comparing.
2Measurement precision
If limited unit size analysis is used, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transitions from limited unit size analysis to sequence-level analysis by introducing an attention mechanism that operates across the entire sequence of tokens. Instead of analyzing fixed-size windows or limited units, the attention mechanism computes relationships between all tokens in the query and candidate answer sequences, capturing long-range dependencies and global context. This dimensional expansion from local units to global sequences improves context capture accuracy.
3Measurement precision
If insufficient attention mechanisms are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary layer between the encoding and matching stages. This attention mechanism serves as a mediator that selectively weights and aggregates information from different tokens based on their relevance to the query and candidate answer. The attention scores act as intermediaries that capture the importance of each token, enabling the model to focus on relevant context while filtering out noise, thereby improving context attention accuracy.
4Measurement precision
If separate independent analyses are used for query and candidate answer, then processing speed is improved, but measurement precision deteriorates
Solution Approach 1:
The patent merges the analysis of query and candidate answer into a single unified attention-based process. Instead of performing separate independent analyses and then matching results, the attention mechanism simultaneously processes both inputs, computing attention scores that reflect their mutual relationships. This combined analysis captures dependencies and interactions between query and candidate answer tokens in one pass, improving relationship capture accuracy while maintaining processing efficiency through shared computations.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that can determine an answer to a query based on matching probabilities for combinations of respective candidate answers. For example, the disclosed systems can utilize a gated-self attention mechanism (GSAM) to interpret inputs that include contextual information, a query, and candidate answers. The disclosed systems can also utilize a memory network in tandem with the GSAM to form a gated self-attention memory network (GSAMN) to refine outputs or predictions over multiple reasoning hops. Further, the disclosed systems can utilize transfer learning of the GSAM/GSAMN from an initial training dataset to a target training dataset.