Abstract
This paper presents a context-oriented system based on ELECTRA for solving math word problems. The system leverages the power of ELECTRA’s pre-trained language model to better understand the context and semantics of mathematical word problems.
This paper introduces a context-oriented system that utilizes ELECTRA, a pre-trained language model, to enhance the understanding and solving of mathematical word problems. The system focuses on capturing contextual information to improve problem comprehension and solution accuracy.
Key Contributions
- ELECTRA Integration: Effective use of ELECTRA for mathematical problem understanding
- Context-Oriented Approach: Emphasis on contextual information for better problem comprehension
- System Architecture: Comprehensive system design for math word problem solving
- Performance Evaluation: Thorough evaluation of the proposed approach
Methodology
The proposed system includes:
- Context Analysis: Deep understanding of problem context using ELECTRA
- Semantic Processing: Enhanced semantic understanding of mathematical concepts
- Problem Decomposition: Breaking down complex problems into manageable components
- Solution Generation: Systematic approach to generating mathematical solutions
- Evaluation Framework: Comprehensive evaluation of system performance
This work demonstrates the effectiveness of transformer-based language models in educational applications, particularly in the domain of automated mathematical problem solving.
Hao Meng
Ph.D. Candidate in Education Technology
I am a Ph.D. candidate in Education Technology at the Faculty of Artificial Intelligence in Education, Central China Normal University. My research focuses on Intelligent Tutoring Systems, Technology Enhanced Learning, and Automated Problem Solvers.