Inter-Document Attention Mechanism for Multi-Document Reasoning
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Solution Overview
Problem
Existing natural language processing approaches struggle to effectively address queries that require reasoning over multiple documents, as they primarily model text sequentially and fail to account for non-sequential relationships between documents, leading to suboptimal performance in extracting answers from distributed evidence.
Innovation Solution
The implementation of an inter-document attention mechanism within a neural network framework that propagates attention from one document to another linked document, incorporating inter-document contextual information into semantic representations to facilitate reasoning across multiple documents, enabling the extraction of answers from supporting evidence spread across multiple documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing natural language processing approaches model text sequentially, then the processing is simple and efficient, but the ability to capture relationships between multiple documents is insufficient
Solution Approach 1:
The patent transitions from sequential one-dimensional text processing to multi-dimensional document relationship modeling by introducing graph structures that represent documents as nodes and relationships as edges, enabling simultaneous capture of intra-document and inter-document contexts
Solution Approach 2:
The patent introduces graph attention mechanisms as intermediaries that mediate between multiple documents, allowing the model to selectively attend to relevant information across document boundaries while maintaining computational tractability
2Reliability
If attention is propagated between multiple documents, then reasoning over multiple documents improves, but computational cost increases
Solution Approach 1:
The patent applies local quality by making attention mechanisms selective rather than uniform, allowing the model to focus computational resources on relevant document pairs and relationships while reducing attention to less important connections
Solution Approach 2:
The patent uses partial action by applying graph attention selectively to specific document relationships identified as important, rather than computing attention across all possible document pairs, thus reducing overall computational burden
3Measurement precision
If contextualized semantic representations are obtained by propagating inter-document attention, then answer accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching document embeddings and attention weights during training, allowing faster inference when answering questions without re-computing all attention relationships from scratch
Data Source
AI summary
This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.