Abstract
This paper presents a bi-channel math word problem solver that combines understanding and reasoning capabilities. The system uses two separate channels to process different aspects of mathematical problem solving, enhancing both comprehension and solution generation.
This paper introduces a novel bi-channel architecture for solving mathematical word problems that separates understanding and reasoning processes. The system leverages two distinct channels to handle different aspects of problem solving, leading to improved performance and interpretability.
Key Contributions
- Bi-channel Architecture: Novel dual-channel approach for math problem solving
- Understanding Channel: Dedicated channel for problem comprehension
- Reasoning Channel: Specialized channel for mathematical reasoning
- Integration Strategy: Effective combination of understanding and reasoning outputs
- Performance Enhancement: Improved accuracy and interpretability
Methodology
The proposed system consists of:
- Understanding Channel: Processes natural language to extract mathematical concepts
- Reasoning Channel: Performs mathematical reasoning and computation
- Feature Extraction: Comprehensive feature extraction from problem text
- Channel Integration: Fusion of understanding and reasoning outputs
- Solution Generation: Final solution synthesis from both channels
Experimental Results
The bi-channel approach demonstrates superior performance compared to single-channel baselines, showing the effectiveness of separating understanding and reasoning processes in mathematical problem solving.
This work contributes to the field of educational AI by providing a more structured and interpretable approach to 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.