Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs{'} specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9{\%} to 69.5{\%} on Math23K with training-test split, from 65.8{\%} to 66.9{\%} on Math23K with 5-fold cross-validation and from 69.2{\%} to 76.1{\%} on MAWPS.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Math Word Problem Solving Math23K GROUP-ATT Accuracy (5-fold) 66.9 # 13
Accuracy (training-test) 69.5 # 10


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