Supported Decision Tree Lattices for Accurate Query Answering
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Solution Overview
Problem
Existing question-answering systems lack the ability to provide accurate and personalized responses by leveraging both attribute-value and semantic representations, limiting their effectiveness in real-world applications.
Innovation Solution
The use of supported decision trees, generated from a corpus of documents, which incorporate rhetorical relationships and additional textual information to enhance decision-making processes, allowing for more robust and precise answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional question-answering systems are used, then the system structure is simple, but the accuracy and personalization of responses deteriorates
Solution Approach 1:
The patent implements nested decision trees where decision nodes contain both attribute-value comparisons and semantic reasoning components. The decision tree structure is nested within a machine reading comprehension framework, with each node potentially containing multiple levels of reasoning support. This nesting allows the system to maintain a unified architectural structure while incorporating multiple layers of processing for improved accuracy.
Solution Approach 2:
The system combines multiple types of representations (attribute-value representations and semantic representations) within a single decision-making framework. The decision trees are composed of different node types that handle different aspects of reasoning, creating a composite decision-making system that leverages both structured data and semantic understanding to improve response accuracy.
2Reliability
If supported decision trees with textual explanations are integrated, then the personalization and robustness of answers improve, but the processing time and computational complexity increase
Solution Approach 1:
The system pre-processes documents to extract and organize attribute-value pairs and semantic relationships before query processing. Decision trees are pre-built and indexed with supporting textual passages, allowing the actual query processing to simply navigate the pre-organized structure rather than performing complex analysis in real-time. This preliminary action significantly reduces processing time during actual question answering.
Solution Approach 2:
The decision-making process is segmented into distinct phases: document processing phase (where textual explanations are extracted and organized), decision tree navigation phase (where the query is matched against pre-processed structures), and answer generation phase. This segmentation allows the system to perform computationally intensive tasks during offline processing while maintaining fast response times during actual query handling.
3Adaptability or versatility
If machine reading comprehension techniques are applied, then the semantic understanding and personalization improve, but the computational resources and processing complexity increase
Solution Approach 1:
Instead of performing full machine reading comprehension analysis on every query, the system creates simplified copies of the decision-making process in the form of decision trees. These decision trees capture the essential semantic relationships and attribute-value connections extracted during initial document processing, allowing queries to be answered by navigating simplified structures rather than re-performing complex computational analysis.
Solution Approach 2:
The system changes the parameter representation from raw textual data to structured attribute-value pairs and semantic relationships. By transforming the data representation during the initial processing phase, the system reduces the computational complexity of subsequent query processing while maintaining the semantic understanding necessary for personalization.
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.