Query Decomposition and Logical Synthesis for Accurate Answers
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Solution Overview
Problem
Current question answering systems face challenges in understanding natural language queries and providing accurate answers from large collections of documents, as they lack human-like natural language processing and common sense reasoning, making it difficult to decompose complex questions into manageable subqueries and synthesize comprehensive answers.
Innovation Solution
The system decomposes input queries into subqueries, conducts searches in multiple databases to identify candidate answers, applies a ranking function to each candidate answer, and uses a logical synthesis component to combine selected answers from subqueries into a final answer, employing natural language processing for both decomposition and synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the system uses traditional search engines to retrieve documents, then it can access large collections of unstructured data, but it cannot provide direct factual answers to complex questions
Solution Approach 1:
The system segments complex questions into multiple sub-queries that can be answered independently. Each sub-query targets specific aspects of the original question, allowing the system to retrieve and synthesize information from multiple sources to form a complete answer.
Solution Approach 2:
The system introduces intermediary components including a query decomposition module that breaks down complex questions, a candidate answer generation module that retrieves potential answers from multiple databases, a logical synthesis component that combines answers, and a ranking module that selects the best answer. These intermediaries transform traditional document retrieval into direct answer provision.
2Measurement precision
If the system decomposes complex questions into subqueries and synthesizes answers from multiple sources, then answer accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary decomposition of complex questions into sub-queries before retrieval, and pre-ranks candidate answers using a ranking function. This preliminary processing organizes the search space and enables more efficient retrieval and synthesis operations.
Solution Approach 2:
The system generates multiple candidate answers from different databases and applies ranking functions to select the most relevant ones. By generating more candidates than strictly necessary and then filtering through ranking, the system ensures high accuracy while managing processing time through selective evaluation.
3Adaptability or versatility
If the system applies natural language processing to understand queries, then it can handle diverse question types, but it lacks human-like understanding and common sense reasoning
Solution Approach 1:
The system employs a universal query decomposition framework that handles multiple question types (fact, list, definition, how, why, hypothetical, semantically-constrained, and cross-lingual questions) through a common architecture. The logical synthesis component applies domain-independent reasoning patterns that work across different question domains.
Solution Approach 2:
The system uses feedback mechanisms where candidate answers are ranked and evaluated against the original query and retrieved evidence. The ranking module provides feedback on answer quality, and the system can iteratively refine its understanding by analyzing which answers best satisfy the query requirements.
Data Source
AI summary
A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, decomposing the input query into a plurality of different subqueries, and conducting a search in one or more data sources to identify at least one candidate answer to each of the subqueries. A ranking function is applied to each of the candidate answers to determine a ranking for each of these candidate answers; and for each of the subqueries, one of the candidate answers to the subquery is selected based on this ranking. A logical synthesis component is applied to synthesize a candidate answer for the input query from the selected the candidate answers to the subqueries. In one embodiment, the procedure applied by the logical synthesis component to synthesize the candidate answer for the input query is determined from the input query.