Question Answering Mode Selection for Higher Answer Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing question answering systems face low accuracy in selecting the target answer from multiple candidate answers, particularly in fields like medical question answering, failing to meet user requirements.
Innovation Solution
A question answering method involving general semantic understanding to obtain data features, selecting a target question answering mode based on these features, and processing the data to determine the target answer, along with a training method that adjusts model parameters using sample labels and knowledge data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single question answering mode is used, then the system is simple to operate, but the accuracy of selecting the target answer is low
Solution Approach 1:
The system dynamically selects different question answering modes based on the characteristics of the input data. A mode selection module determines whether to use a rule-based mode or a machine learning-based mode by analyzing data features, allowing the system to adapt its processing approach to each specific case rather than using a fixed single mode
Solution Approach 2:
The question answering system is divided into multiple independent modules: a rule-based question answering mode, a machine learning-based question answering mode, and a mode selection module. Each module handles specific types of queries independently, and the selection module routes appropriate queries to the most suitable mode, improving overall accuracy while maintaining manageable complexity
2Measurement precision
If multiple question answering modes are introduced, then the accuracy of selecting the target answer is improved, but the device complexity increases
Solution Approach 1:
The mode selection module automatically analyzes the input data features and selects the most appropriate question answering mode without requiring user intervention. The system self-determines whether to apply rule-based or machine learning-based processing based on inherent data characteristics, maintaining ease of operation while utilizing multiple modes
3Measurement precision
If domain-specific knowledge data is integrated, then the professionalism and accuracy of question answering is enhanced, but the quantity of data to be processed increases
Solution Approach 1:
Domain-specific knowledge data is integrated into specific modules where it is most effective: the rule-based mode uses structured domain knowledge for deterministic processing, while the machine learning mode uses domain data for training. This localized integration ensures knowledge is applied where it provides maximum value without unnecessarily processing all data through all modules
Data Source
AI summary
A question answering method, a method of training a question answering model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing technology, deep learning technology, and knowledge mapping technology. The question answering method includes: obtaining data to be processed, wherein the data to be processed includes a question and candidate answers; performing general semantic understanding on the data to be processed to obtain a general data feature; selecting a target question answering mode from candidate question answering modes based on the general data feature; and processing the general data feature by using the target question answering mode, to obtain a target answer for the question from the candidate answers.