Question Decomposition Engine for Open Domain QA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automated question answering systems struggle to provide accurate, real-time responses to open-domain questions that are subjective, emotionally charged, or context-dependent, as they fail to understand the focus, emotion, and subjectivity of questions, leading to inadequate information alignment with the questioner's intent.
Innovation Solution
A system that decomposes user questions into sub-questions and contexts, generates question-context pairs, and queries answer resources in parallel to produce question-context-answer triples, using a neural network to select and extract relevant information for human-like, concise responses, while considering user-defined priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing QA systems focus on knowledge-driven, factoid-based questions, then answer accuracy is improved, but the system cannot handle subjective, emotionally charged, or context-dependent questions
Solution Approach 1:
The system segments complex open-domain questions into multiple sub-questions and identifies relevant contexts for each sub-question. This decomposition allows the system to handle subjective and context-dependent questions by breaking them down into manageable components that can be answered individually while maintaining overall accuracy.
Solution Approach 2:
The system adds a contextual dimension to traditional QA by incorporating context identification and alignment. Instead of treating questions in isolation, the system considers multiple contexts (e.g., user intent, emotional tone, situational factors) as additional dimensions, enabling it to handle subjective and emotionally charged questions effectively.
2Productivity
If the system decomposes questions into sub-questions and queries answer resources in parallel, then real-time response capability is improved, but system complexity increases
Solution Approach 1:
The system divides the question processing task into separate modules: question decomposition, context identification, parallel query execution, and answer synthesis. This segmentation allows each module to operate independently and efficiently, achieving real-time response capability while managing complexity through modular design.
Solution Approach 2:
The system employs automated mechanisms for question decomposition, context alignment, and answer selection that operate without manual intervention. The parallel query engine automatically manages multiple answer resources, and the system self-coordinates the integration of results, reducing operational complexity despite the sophisticated architecture.
3Ease of operation
If the system generates human-like, concise answers, then user satisfaction is improved, but the complexity of answer generation increases
Solution Approach 1:
The system introduces intermediate structures (question-context pairs and question-context-answer triples) that mediate between the raw question and the final human-like answer. These intermediaries organize information in a structured format that facilitates natural language generation while managing the complexity of producing concise, human-like responses.
Solution Approach 2:
The system performs preliminary processing of questions into standardized formats and pre-organizes answer information before final generation. By decomposing questions and aligning contexts in advance, the system prepares structured data that simplifies the subsequent answer generation process, enabling human-like output without overwhelming complexity.
Data Source
AI summary
A system for automated question answering, comprising: a user interface configured to receive a query from a user; a question decomposition engine configured to decompose the query into one or more sub-questions and one or more contexts, and to align the sub-questions with contexts to generate question-context pairs; a query engine configured to query one or more answer resources with the question-context pairs to identify information likely to comprise an answer; and an answer generator configured to: (i) generate question-context-answer triples using the identified information from the query engine; (ii) select a generated question-context-answer triple comprising information most likely to comprise an answer to the identified sub-question; (iii) extract from the selected question-context-answer triple a portion of the associated information comprising an answer to the identified sub-question; and (iv) generate a natural language answer comprising a response to the query posed by the user.