  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. We
further propose a novel approach with logical prompt and interpretation
generation, called LogicSolver. For each MWP, our LogicSolver first retrieves
some highly-correlated algebraic knowledge and then passes them to the backbone
model as prompts to improve the semantic representations of MWPs. With these
improved semantic representations, our LogicSolver generates corresponding
solution expressions and interpretable knowledge formulas in accord with the
generated solution expressions, simultaneously. Experimental results show that
our LogicSolver has stronger logical formula-based interpretability than
baselines while achieving higher answer accuracy with the help of logical
prompts, simultaneously. The source code and dataset is available at
https://github.com/yangzhch6/InterMWP.
