Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions (P19-1)
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| Challenge: | Several deep learning models have been proposed for solving math word problems (MWPs) but their approaches to capturing features are not specifically designed for MWP. |
| Approach: | They propose to use a group attention mechanism to extract global features, quantity-related features, quantities-pair features and question-related feature in MWPs. |
| Outcome: | The proposed approach performs significantly better than previous state-of-the-art methods and boosts performance from 66.9% to 69.5% on Math23K with training-test split, from 65.8% to 66.99% on Math 23K with 5-fold cross-validation and from 69.99% to 76.1% on MAWPS. |
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| Challenge: | Existing solvers for math word problems often achieve high performance on benchmark datasets . existing models rely on shallow heuristics to achieve high accuracy . |
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| Challenge: | Experiments show that even quite powerful LLMs are still challenged by MWPs. |
| Approach: | They propose to analyze what makes math word problems (MWPs) in English challenging for large language models (LLMs). |
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| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
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| Challenge: | Existing studies lack diversity in problem types, lexical usage patterns, languages, and intermediate solution forms for the math word problem. |
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| Challenge: | Existing models memorize procedures from context and rely on shallow heuristics to solve MWPs. |
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| Challenge: | Existing approaches to solve math word problems do not provide explanations for generated expressions. |
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
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WARM: A Weakly (+Semi) Supervised Math Word Problem Solver (2022.coling-1)
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| Challenge: | Existing approaches to solving math word problems require full supervision in the form of intermediate equations. |
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