Question Answering System Evidence Chain Linker
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Solution Overview
Problem
Current computerized question answering systems face challenges in handling both single-hop and multi-hop question answering scenarios, particularly when information is scattered across heterogeneous sources like unstructured text and structured tables, leading to poor performance in real-world applications with diverse knowledge sources.
Innovation Solution
A question answering system is developed that includes a retriever subsystem for identifying relevant text evidence, a linker subsystem to associate evidence strings with secondary evidence, and a chainer subsystem to rank evidence chains based on relevance, along with a reader subsystem to generate answers, effectively handling both single-hop and multi-hop queries by forming and evaluating evidence chains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If information is scattered across heterogeneous sources (unstructured text and structured tables), then the system can access more diverse knowledge, but the complexity of fusing information from multiple sources increases significantly
Solution Approach 1:
The system segments the complex task of multi-source information fusion into distinct subsystems: a retriever subsystem that identifies relevant text evidence, a linker subsystem that associates evidence strings with secondary evidence to form evidence chains, and a reader subsystem that generates answers. This segmentation allows each subsystem to handle specific aspects of the problem independently, reducing overall system complexity while maintaining the ability to process diverse knowledge sources.
Solution Approach 2:
The system introduces evidence chains as an intermediary structure that connects disparate evidence sources. The linker subsystem creates these evidence chains by associating primary evidence strings with secondary evidence, forming a structured representation that bridges unstructured text and structured tables. This intermediary structure simplifies the fusion process by providing a standardized format for combining information from heterogeneous sources.
2Reliability
If the system uses multiple evidence sources to answer questions, then the accuracy of answers improves, but the time and computational resources required increase
Solution Approach 1:
The retriever subsystem performs preliminary action by identifying and retrieving only the most relevant text evidence strings before the main processing occurs. This pre-filtering step reduces the volume of evidence that needs to be processed in subsequent stages, thereby maintaining answer accuracy while reducing processing time and computational resources.
Solution Approach 2:
The system applies partial action by focusing computational resources on the most promising evidence chains. The chainer subsystem evaluates evidence chains and selects those with the highest relevance scores, processing only a subset of all possible evidence combinations. This approach achieves high accuracy by concentrating resources on the most likely correct answers rather than exhaustively processing all evidence sources.
3Adaptability or versatility
If the system forms evidence chains by associating multiple evidence strings, then the ability to answer complex multi-hop questions improves, but the device complexity increases
Solution Approach 1:
The system implements dynamic evidence chain construction where the structure and length of evidence chains adapt based on the specific question being asked. The linker subsystem dynamically associates evidence strings based on question context, creating variable-length chains that match the complexity requirements of each query. This dynamic approach enables the system to handle both simple single-hop and complex multi-hop questions without requiring a fixed, overly complex structure for all cases.
Solution Approach 2:
The evidence chain structure employs a nested organization where primary evidence strings are associated with secondary evidence strings, which may themselves contain nested evidence. This hierarchical nesting allows the system to represent complex multi-hop relationships in a structured manner, where each level of nesting corresponds to a hop in the reasoning chain. The nested structure simplifies the management of complex evidence relationships by organizing them in a hierarchical fashion that mirrors the logical structure of multi-hop reasoning.
Data Source
AI summary
A method for computer question answering includes, at a retriever subsystem of a question answering computer system, identifying a plurality of relevant text evidence strings for an input text question. At a linker subsystem of the question answering computer system, one or more of the plurality of relevant text evidence strings are associated with a respective secondary text evidence string to form a plurality of evidence chains via a previously-trained entity-linking machine-learning model. At a chainer subsystem of the question answering computer system, a ranked set of the evidence chains is identified based at least in part on an output of a generative machine-learning model applied to each of the plurality of evidence chains. At a reader subsystem of the question answering computer system, an answer to the input text question is output based at least in part on the ranked set of evidence chains.