2022

LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning

Yang, Zhicheng, Qin, Jinghui, Chen, Jiaqi et al.

Understand

Recently, deep learning models have made great progress in MWP solving on answer accuracy.

However, they are uninterpretable since they mainly rely on shallow heuristics to achieve high performance without understanding and reasoning the grounded math logic.
To address this issue and make a step towards interpretable MWP solving, we first construct a high-quality MWP dataset named InterMWP which consists of 11,495 MWPs and annotates interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation.
Different from existing MWP datasets, our InterMWP benchmark asks for a solver to not only output the solution expressions but also predict the corresponding logical formulas.

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