Dynamic Semantic Network for Context-Aware Question Answering
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Solution Overview
Problem
Existing solutions for natural language understanding and question answering rely heavily on annotated data and handcrafted rules, making them expensive, difficult to manage, and ineffective in incorporating context information, especially in interactive dialogue scenarios.
Innovation Solution
A dynamic semantic network system that uses a bidirectional LSTM and multilayer LSTM controller to generate vectors for query words, combining responses from static and dynamic memory sources through an interpolation gate, allowing for efficient context representation and reduced reliance on annotated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing solutions use massive amounts of annotated data to train domain-specific intents, then question answering capability is improved, but system cost and complexity increase significantly
Solution Approach 1:
The patent replaces the mechanical system of handcrafted rules and annotated data training with a neural network-based semantic network that automatically learns from unstructured text. The neural network processes natural language queries and generates answers through learned semantic relationships rather than predefined rules, reducing system complexity while maintaining answer quality.
Solution Approach 2:
The system enables self-service by automatically constructing semantic networks from unstructured text sources without requiring manual annotation. The neural network autonomously learns semantic relationships and generates answers, eliminating the need for expensive annotated datasets and manual rule creation while maintaining reliable question-answering capability.
2Device complexity
If handcrafted rules are used for natural language understanding, then system simplicity is maintained, but adaptability to context information deteriorates
Solution Approach 1:
The patent implements dynamics by using a neural network that adaptively adjusts its semantic representations based on input queries and context. The semantic network dynamically processes different types of questions and incorporates relevant context information from the knowledge base, allowing the system to adapt to various query types and contexts while maintaining a unified architecture that doesn't overly complicate the system.
3Reliability
If neural Turing machines use distributed word representation, then semantic understanding is improved, but computational requirements and data needs increase
Solution Approach 1:
The patent replaces the neural Turing machine architecture with a simplified semantic network that uses distributed word representations through neural embeddings. This substitution maintains the ability to capture semantic relationships while reducing computational complexity and data requirements by using more efficient neural network structures that don't require the full Turing machine machinery.
Data Source
AI summary
A computer-implemented method of answering questions comprises: receiving, by one or more processors, a query; based on the query, generating, by the one or more processors, a matrix; based on the matrix, modifying, by the one or more processors, a dynamic memory; based on the matrix, determining, by the one or more processors, a first response from the dynamic memory; based on the matrix, determining, by the one or more processors, a second response from a database; based on the first response and the second response, determining, by the one or more processors, a third response; and in response to the query, providing, by the one or more processors, the third response.