Automated Question Generation Using Semantic Role Labeling and RNNs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge lies in automating the generation of questions from electronic written content at scale, as human-created questions do not translate well to computer instructions, and simple machine learning algorithms are not accurate enough to produce understandable and relevant questions due to the complexity of languages.
Innovation Solution
The use of semantic role labeling and recurrent neural networks (RNNs) for identifying answer phrases and generating questions, with user feedback used to improve question quality by updating parameters and weights in the RNNs, along with a system that generates Frequently Asked Questions documents and employs gap-filling and co-reference resolution techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human experts manually create questions from electronic content, then question quality and relevance are improved, but productivity and scalability deteriorate
Solution Approach 1:
The system uses recurrent neural networks to learn and copy the question-generation process from human experts. By training on examples of human-created questions and their corresponding content, the RNN captures the patterns and techniques experts use, enabling automated generation of high-quality questions that replicate human expertise at scale
Solution Approach 2:
The patent replaces the mechanical human cognitive process of question creation with an automated computational system. The RNN-based machine learning model substitutes human experts' manual analysis and question formulation with algorithmic processing, achieving both high quality and scalability
2Productivity
If simple machine learning algorithms are used for automated question generation, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system transitions from simple machine learning algorithms to recurrent neural networks with multiple adjustable parameters and weights. The RNN architecture introduces complex parameters including hidden states, activation functions, and training epochs, enabling the system to capture nuanced linguistic patterns and generate questions with high accuracy
Solution Approach 2:
The patent combines multiple technical components into a composite question-generation system: semantic role labeling, recurrent neural networks, and training mechanisms work together synergistically. This composite approach integrates different processing layers to achieve both automation and high question quality
3Manufacturing precision
If complex recurrent neural networks are used for question generation, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The complex question-generation task is segmented into distinct processing stages: semantic role labeling to identify answer phrases, RNN-based question formulation, and training phases. This segmentation breaks down the complex RNN system into manageable functional components, making the overall system more tractable despite the underlying complexity
4Productivity
If automated question generation is implemented at scale, then productivity is improved, but loss of information increases
Solution Approach 1:
The system implements feedback mechanisms where generated questions are evaluated and used to retrain and refine the RNN model. This iterative feedback loop allows the system to learn from its mistakes, progressively improving its understanding of language nuances while maintaining high-volume automated generation capability
Data Source
AI summary
In an example embodiment, factual question generation from freeform content is achieved through semantic role labeling and recurrent neural networks (RNNs). Specifically, semantic role labeling is used to identify an answer phrase so that it can be replaced with an appropriate question word. RNNs are then used to extract triples (Subject-Object-Predicate) from the sentence, and each of these triples can be used as an answer phrase/word. An RNN is then fed with training data to generate the questions more efficiently.