Hybrid Question-Answering System with Classification Model
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Solution Overview
Problem
Automatic question-answering systems face challenges in generating accurate answers to complex questions in natural language due to reliance on proper question formulation and the need for large training datasets, leading to inefficiencies and increased operational costs in consumer support.
Innovation Solution
A hybrid question-answering system combining retrieval and deep QA applications, with a question classification model to determine the most suitable approach for each question, leveraging machine learning models to choose between retrieval and deep learning methods based on confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a retrieval-based QA system formulates search queries to answer questions, then it can handle simple questions effectively, but it fails to properly formulate search queries for complex questions
Solution Approach 1:
The patent combines retrieval-based QA and deep learning-based QA into a hybrid system. The retrieval QA component handles simple questions through search query formulation, while the deep learning component handles complex questions through semantic understanding. A question classification model determines which component to use for each question, enabling the system to leverage the strengths of both approaches and achieve high reliability across different question types.
2Adaptability or versatility
If a QA system relies on machine learning to answer complex questions, then it can handle diverse question types, but it lacks proper training data and models
Solution Approach 1:
The hybrid QA system merges retrieval-based QA with pre-trained language models. The retrieval component provides accurate answers for straightforward questions, while the deep learning component with access to training data handles complex, diverse questions. This combination ensures that the system has proper training resources available when needed, improving reliability for complex question types.
3Reliability
If a hybrid QA system uses both retrieval and deep learning approaches, then it can answer both simple and complex questions accurately, but it increases system complexity
Solution Approach 1:
The patent introduces a question classification model as an intermediary component that receives the input question and determines whether to route it to the retrieval QA component or the deep learning QA component. This mediator simplifies the overall system architecture by providing a clear decision-making mechanism, preventing the need for complex integration logic between the two QA approaches.
Solution Approach 2:
The hybrid QA system segments the question-answering task into two distinct pathways: retrieval-based processing for simple questions and deep learning-based processing for complex questions. The question classification model divides the input stream based on question characteristics, allowing each component to specialize in its optimal domain and reducing the complexity of managing a single monolithic system.
4Productivity
If a QA system fails to return relevant answers, then consumers may request human agents, but this increases operational costs
Solution Approach 1:
The hybrid QA system enables self-service by automatically answering both simple and complex consumer questions without human intervention. The retrieval component handles routine inquiries, while the deep learning component handles complex scenarios, allowing the system to serve itself across the full spectrum of question types. This reduces the need for human agent escalation and lowers operational costs associated with manual customer support.
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for a hybrid question-answering (QA) application are provided. The hybrid QA application includes both a retrieval QA application and a deep QA application, as well as a question classification model that determines whether the retrieval QA application or the deep QA application can generate a better answer to an input question.