Attention Mechanism for Sentence Graphs
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Solution Overview
Problem
Conventional attention-based neural networks for natural language processing face high computation complexity and fail to effectively model syntactic information, leading to poor performance in tasks like sequence annotation and semantic role labeling.
Innovation Solution
A sentence graph is generated with nodes representing words and edges indicating syntactic relationships, allowing an attention mechanism to focus on neighbor nodes connected by edges, reducing computation cost and leveraging syntactic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional attention mechanisms are applied to all word pairs in a sentence, then comprehensive semantic information can be captured, but computation complexity becomes prohibitively high
Solution Approach 1:
The patent segments the attention computation by dividing words into different heads based on their positional relationships. Each head processes a specific subset of words (e.g., words within certain distance ranges), rather than computing attention between all word pairs. This segmentation reduces the computational complexity from O(n²) to O(n), while still capturing comprehensive semantic information through the aggregation of multiple heads.
Solution Approach 2:
The patent introduces a new dimension to the attention mechanism by adding positional relationship-based segmentation. Instead of a single flat attention computation, the system creates multiple attention heads organized in a hierarchical structure based on positional distances. This dimensional organization allows efficient computation while preserving semantic relationships.
2Adaptability or versatility
If conventional attention mechanisms are used, then all words are treated equally, but syntactic information between words is not effectively modeled
Solution Approach 1:
The patent applies local quality by creating different attention heads with different functionalities based on positional relationships. Each head is specialized to capture specific types of syntactic relationships (e.g., local dependencies, long-range dependencies, subject-object relationships). This allows the system to effectively model syntactic information by assigning different 'qualities' or functions to different parts of the attention mechanism, thereby improving performance in sequence annotation and semantic role labeling tasks.
3Loss of information
If attention is computed over the entire sentence, then global context is captured, but computation cost increases significantly for long sentences
Solution Approach 1:
The patent segments the global attention computation into multiple local attention heads that each process subsets of words based on positional relationships. Instead of computing attention between all word pairs in a long sentence (which would be computationally expensive), the system divides the work into manageable segments that can be processed efficiently in parallel, reducing computation cost while maintaining global context through aggregation.
Solution Approach 2:
The patent transforms the single-dimensional global attention computation into a multi-dimensional structure organized by positional relationships. This dimensional transformation allows the system to capture global context through hierarchical aggregation of local patterns, achieving the same effect with reduced computational cost by processing information in organized dimensions rather than brute-force pairwise comparisons.
Data Source
AI summary
Embodiments of the present disclosure relate to attention-based neural language processing. In an embodiment, a method is disclosed. According to the method, a sentence graph is generated from a sentence containing words. The sentence graph comprises nodes representing words and edges connecting the nodes, at least one of the edges being constructed to indicate a syntactic relationship between words represented by nodes connected therebetween. Word representations for the words are determined based on the sentence graph by applying an attention mechanism on respective ones of the nodes and respective sets of neighbor nodes for the nodes. A set of neighbor nodes for a node has edges connected to the node. A sentence representation for the sentence is determined based on the word representations for use in a natural language processing task related to the sentence. In other embodiments, a system and a computer program product are disclosed.