Papers by Bing Tian Dai

1 papers
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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