Solving Arithmetic Word Problems with a Templatebased Multi-Task Deep Neural Network (T-MTDNN)

Solving arithmetic word problem automatically has been a challenge both in terms of attaining robustness to unseen problems and achieving high problem-solving accuracy. In this paper, we propose a Template based - Multi-Task Deep Neural Network (T-MTDNN) framework, which utilizes two types of techniques. First, by generating normalized equation templates, we achieve robustness by enabling a more general language representation of a given linguistic task. Second, by applying MTDNN [1], which uses BERT with number and operator classification as multi-tasks, we gain higher problem solving accuracy compared to T-RNN [2], which is the state-of-the-art model. Specifically, with MAWPS dataset, the accuracy of T-MTDNN is 78.88% compared to the accuracy of T-RNN at 66.8%. With Math23K dataset, the accuracy of T-MTDNN is 72.6% compared to the accuracy of T-RNN at 66.9%.