Multi-domain Question Answering System with Semantic Decomposition
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Solution Overview
Problem
Existing automated question answering systems struggle to provide human-like, accurate responses to subjective and multi-domain questions in real-time, 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 utilizing a deep learning-based ensemble technique with a bidirectional Recurrent Neural Network (RNN) architecture for question decomposition, combined with latent semantic analysis (LSA) and latent Dirichlet allocation (LDA) to identify similar questions across a semantic space, extracting and ranking answers based on domain and focus, and fine-tuning responses for real-time human-like output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing QA systems focus on factoid-based questions, then answer accuracy is improved, but the ability to handle subjective and multi-domain questions deteriorates
Solution Approach 1:
The system segments a question into multiple components including domain identification, focus identification, and subjectivity detection. This segmentation allows the system to handle different aspects of subjective and multi-domain questions separately, improving both accuracy and adaptability simultaneously
Solution Approach 2:
The QA system is designed with multi-functional capabilities to handle both factoid-based and subjective questions using a unified architecture. The system can adaptively switch between different processing modes based on question type, enabling it to maintain high accuracy across diverse question domains
2Loss of information
If the system provides comprehensive answers, then information completeness is improved, but real-time response capability deteriorates
Solution Approach 1:
The system performs preliminary processing by pre-identifying question domains and focus areas before generating answers. This preliminary analysis enables faster retrieval of relevant information from knowledge bases, ensuring both completeness and real-time responsiveness
Solution Approach 2:
The system extracts only the most relevant information needed to answer the question based on identified domain and focus, rather than retrieving all possible information. This selective extraction maintains information completeness while significantly reducing processing time for real-time responses
3Loss of information
If the system analyzes question focus and subjectivity, then alignment with questioner intent is improved, but system complexity deteriorates
Solution Approach 1:
The complex task of understanding questioner intent is segmented into distinct modules: domain classification, focus extraction, and subjectivity analysis. Each module handles a specific aspect independently, making the overall system more manageable and easier to implement despite the sophisticated functionality required
Data Source
AI summary
A system (1000) for automated question answering, including: semantic space (210) generated from a corpus of questions and answers; a user interface (1030) configured to receive a question; and a processor (1100) comprising: (i) a question decomposition engine (1050) configured to decompose the question into a domain, a keyword, and a focus word; (ii) a question similarity generator (1060) configured to identify one or more questions in a semantic space using the decomposed question; (iii) an answer extraction and ranking engine (1080) configured to: extract, from the semantic space, answers associated with the one or more identified questions; and identify one or more of the extracted answers as a best answer; and (iv) an answer tuning engine (1090) configured to fine-tune the identified best answer using one or more of the domain, keyword, and focus word; wherein the fine-tuned answer is provided to the user via the user interface.