Solving math word problems is a challenging task that requires accurate
natural language understanding to bridge natural language texts and math
expressions. Motivated by the intuition about how human generates the equations
given the problem texts, this paper presents a neural approach to automatically
solve math word problems by operating symbols according to their semantic
meanings in texts. This paper views the process of generating equation as a
bridge between the semantic world and the symbolic world, where the proposed
neural math solver is based on an encoder-decoder framework. In the proposed
model, the encoder is designed to understand the semantics of problems, and the
decoder focuses on tracking semantic meanings of the generated symbols and then
deciding which symbol to generate next. The preliminary experiments are
conducted in a dataset Math23K, and our model significantly outperforms both
the state-of-the-art single model and the best non-retrieval-based model over
about 10% accuracy, demonstrating the effectiveness of bridging the symbolic
and semantic worlds from math word problems.

本文提出了一个神经网络模型，基于编码器 - 解码器框架，利用自然语言理解桥接语义世界和符号世界，自动解决数学应用问题，并在 Math23K 数据集上验证模型的有效性。