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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Automatically solving math word problems (MWPs) is a complex AI challenge.
    • Existing methods often oversimplify MWPs as mere word sequences, lacking precision.
    • Human problem-solving involves hierarchical understanding and leveraging related experiences.

    Purpose of the Study:

    • To develop an AI-based math word problem solver that mimics human cognitive processes.
    • To enhance the accuracy and robustness of mathematical expression generation from MWPs.
    • To enable AI to learn from the relationships between different MWPs.

    Main Methods:

    • Proposed a Hierarchical Math Solver (HMS) with a novel encoder for word-clause-problem semantics.
    • Developed a goal-driven, tree-based decoder incorporating knowledge for expression generation.
    • Extended HMS to Relation-enHanced Math Solver (RHMS) using a meta-structure tool and graph to associate related MWPs.

    Main Results:

    • The Hierarchical Math Solver (HMS) effectively exploits semantic dependencies within individual MWPs.
    • The Relation-enHanced Math Solver (RHMS) demonstrated superior accuracy and robustness by utilizing relationships between MWPs.
    • Extensive experiments on large datasets validated the effectiveness of both proposed methods.

    Conclusions:

    • The proposed HMS and RHMS significantly advance the state-of-the-art in automatic math word problem solving.
    • Mimicking human-like hierarchical understanding and experience association leads to more capable AI solvers.
    • RHMS offers a promising direction for building more intelligent and adaptable AI systems for complex reasoning tasks.