Supported Decision-Tree Lattices for Contextual Answer Generation
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Solution Overview
Problem
Existing machine reading comprehension systems struggle to provide accurate and personalized answers to complex queries by relying solely on attribute-value associations, lacking the ability to explain decisions and provide contextual background support.
Innovation Solution
The development of supported decision trees that incorporate rhetorical relationships, linguistic cues, and additional information from text to enhance decision-making processes, allowing for a lattice of decision trees to identify relevant passages and generate more robust answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine reading comprehension systems rely solely on attribute-value associations, then the system complexity is reduced, but the accuracy and personalization of answers to complex queries deteriorates
Solution Approach 1:
The patent segments the decision-making process into multiple decision trees, each handling specific aspects of query processing. These decision trees are organized in a lattice structure that allows systematic exploration of different decision paths, enabling accurate handling of complex queries while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The patent transitions from flat attribute-value associations to a multi-dimensional lattice structure of decision trees. This dimensional transformation enables the system to capture contextual relationships and rhetorical connections between different pieces of information, significantly improving answer accuracy for complex queries while organizing complexity in a hierarchical manner.
2Loss of information
If machine reading comprehension systems use only attribute-value associations, then the ease of operation is improved, but the ability to explain decisions and provide contextual background deteriorates
Solution Approach 1:
The patent performs preliminary organization of information into decision trees during the indexing phase, establishing rhetorical relationships and contextual connections before query processing occurs. This preliminary structuring enables rich contextual explanations to be readily retrieved and presented during actual query processing without significantly increasing operational complexity.
Solution Approach 2:
The lattice structure of decision trees serves as an intermediary between the raw textual information and the final answers. It mediates by organizing and pre-relating information according to rhetorical relationships, enabling the system to provide contextual background and explanations while maintaining efficient query processing through structured navigation.
3Reliability
If the system generates a lattice of supported decision trees from a corpus of documents, then the robustness and precision of machine reading comprehension is improved, but the quantity of processed information increases
Solution Approach 1:
The patent applies local quality by creating decision trees focused on specific local aspects or dimensions of the information corpus. Each decision tree in the lattice handles a particular aspect of query processing, allowing the system to maintain high reliability for specific tasks while managing the overall quantity of processed information through targeted organization rather than monolithic processing.
Solution Approach 2:
By transforming the information corpus into a multi-dimensional lattice structure of decision trees, the patent organizes large quantities of information in a hierarchical manner. This dimensional organization enables efficient navigation and retrieval, allowing the system to process substantial information volumes while maintaining high robustness and precision through structured relationships between information elements.
Data Source
AI summary
Systems, devices, and methods discussed herein are directed to generating an answer to an input query using machine reading comprehension techniques and a lattice of supported decision trees. A supported decision tree can be generated from the various decision chains (e.g., a sequence of elements comprising a premise and a decision connected by rhetorical relationships), where the nodes of the decision tree are identified from the plurality of decision chains and ordered based on a set of predefined priority rules. A lattice may include nodes that individually correspond to a respective supported decision tree. Nodes of the lattice may be identified for an input query. The passages corresponding to those nodes may be obtained and an answer for the query may be generated from the obtained passages using machine reading comprehension techniques. The generated answer may be provided in response to the query.