Modeling Intra-Relation in Math Word Problems with Different
Functional Multi-Head Attentions
Abstract
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, quantitypair 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.