Recall and Learn: A Memory-augmented Solver for Math Word Problems
Shifeng Huang, Jiawei Wang, Jiao Xu, Da Cao, Ming Yang

TL;DR
This paper introduces a memory-augmented, analogy-based framework for solving math word problems, enhancing generalization over template-based methods by recalling and learning from past exercises.
Contribution
It proposes a novel human-like analogical learning method with modules for memory, representation, analogy, and reasoning, improving over existing approaches.
Findings
Outperforms state-of-the-art methods on two datasets.
Demonstrates strong generalization capabilities.
Effective retrieval and analogy modules enhance problem-solving accuracy.
Abstract
In this article, we tackle the math word problem, namely, automatically answering a mathematical problem according to its textual description. Although recent methods have demonstrated their promising results, most of these methods are based on template-based generation scheme which results in limited generalization capability. To this end, we propose a novel human-like analogical learning method in a recall and learn manner. Our proposed framework is composed of modules of memory, representation, analogy, and reasoning, which are designed to make a new exercise by referring to the exercises learned in the past. Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module. Moreover, to solve the problem in a way of analogy, an analogy module and a…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Artificial Intelligence in Games
