Neuro-Symbolic Question Answering via AMR Graph Transformation
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Solution Overview
Problem
Current knowledge base question answering (KBQA) systems face challenges in complex question understanding, the need for reasoning, and the lack of large end-to-end training datasets, particularly in handling multi-hop questions and unusual expressions, with existing approaches suffering from error propagation and requiring extensive expert knowledge or artifacts.
Innovation Solution
The Neuro-Symbolic Question Answering (NSQA) system leverages Abstract Meaning Representation (AMR) parses for task-independent question understanding, employing a pipeline-based approach that transforms AMR into candidate logical queries aligned with the knowledge base, using Logical Neural Networks for reasoning, and integrating reusable modules trained for specific tasks, thereby reducing the need for end-to-end training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end-to-end training approaches are used for KBQA, then question answering performance may improve, but the need for large training datasets and computational resources increases
Solution Approach 1:
The system segments the KBQA task into distinct modules: AMR parsing module, query generation module, and reasoning module. Each module is trained independently on specific sub-tasks, eliminating the need for large end-to-end training datasets while maintaining overall system performance through modular composition.
Solution Approach 2:
The patent introduces AMR (Abstract Meaning Representation) as an intermediary representation layer between natural language questions and knowledge base queries. This intermediary enables task-independent question understanding and facilitates transformation into candidate logical queries, reducing dependency on large training data for the entire pipeline.
2Quantity of substance
If pipeline approaches with pre-trained semantic parsers are used, then data requirements are reduced, but error propagation occurs
Solution Approach 1:
The system incorporates feedback mechanisms where the reasoning module evaluates candidate logical queries against the knowledge base and provides corrections to the query generation module. This feedback loop enables error detection and correction, preventing error propagation while maintaining the efficiency of the pipeline architecture.
3Adaptability or versatility
If complex reasoning is required for multi-hop questions, then question understanding capability improves, but system complexity increases
Solution Approach 1:
The reasoning task is segmented into multiple independent reasoning steps, each handled by separate modules. The query generation module creates candidate queries based on AMR parsing, and the reasoning module processes these queries independently, allowing complex multi-hop reasoning to be decomposed into manageable segments that reduce overall system complexity.
Data Source
AI summary
One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.