Document Graph Reranking for Open-Domain QA Relevance Gaps
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Solution Overview
Problem
Existing methods for Open-Domain Question Answering (ODQA) fail to effectively utilize connections between documents during reranking, leading to the disregard of documents containing answers with less apparent connections to the question context, and incur high computational costs due to redundant information integration.
Innovation Solution
A reranking method using graph neural networks constructs document graphs where nodes represent documents and edges represent common concepts, selectively encoding connection information through message-passing mechanisms to identify relevant documents, reducing computational footprint while improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge graphs and AMR graphs are integrated into learning models for ODQA, then semantic information handling capability is improved, but computational time and GPU memory cost increase
Solution Approach 1:
The patent extracts and utilizes pre-computed AMR graphs from the retrieval stage, taking out only the essential connection information needed for reranking rather than重新 computing full semantic representations. This extraction approach maintains semantic handling capability while reducing computational overhead during the critical reranking phase.
Solution Approach 2:
The patent performs AMR graph generation and semantic analysis as preliminary actions during the document retrieval stage, before the actual reranking process. By preparing semantic representations in advance, the system avoids redundant computations during reranking, thus reducing computational time and GPU memory cost while preserving semantic information handling capability.
2Measurement precision
If AMR graphs are used to construct embeddings for text-level relations, then semantic understanding is improved, but computational resources are consumed
Solution Approach 1:
The patent applies local quality by using AMR graphs selectively and locally - only for documents that require semantic disambiguation during reranking. Rather than applying full AMR-based embedding construction to all documents, the system focuses computational resources on specific documents where semantic understanding provides the most value, thus improving semantic understanding while reducing overall computational resource consumption.
3Device complexity
If connections between documents are not utilized during reranking, then computational simplicity is maintained, but documents with answers but less apparent connections are disregarded
Solution Approach 1:
The patent introduces AMR graphs as an intermediary representation that bridges the gap between simple text matching and complex semantic understanding. By using AMR graphs as a mediator, the system can efficiently capture document connections and semantic relationships without requiring full-blown knowledge graph integration, thus improving document relevance identification while maintaining reasonable computational simplicity.
Data Source
AI summary
A computer-implemented method includes: in response to receiving a query, retrieving a plurality of documents; generating a first graph representation which graphically represents the plurality of documents utilizing a plurality of nodes indicating a concept and a plurality of edges indicating a relationship between at least two nodes among the plurality of nodes; generating a second graph representation with respect to the plurality of documents, based on connection information associated with the first graph representation; ranking the plurality of documents, based on the second graph representation; and applying one or more machine-learned models to generate a response to the query based on the plurality of documents ranked based on the second graph representation.