  The art of mathematical reasoning stands as a fundamental pillar of
intellectual progress and is a central catalyst in cultivating human ingenuity.
Researchers have recently published a plethora of works centered around the
task of solving Math Word Problems (MWP) $-$ a crucial stride towards general
AI. These existing models are susceptible to dependency on shallow heuristics
and spurious correlations to derive the solution expressions. In order to
ameliorate this issue, in this paper, we propose a framework for MWP solvers
based on the generation of linguistic variants of the problem text. The
approach involves solving each of the variant problems and electing the
predicted expression with the majority of the votes. We use DeBERTa
(Decoding-enhanced BERT with disentangled attention) as the encoder to leverage
its rich textual representations and enhanced mask decoder to construct the
solution expressions. Furthermore, we introduce a challenging dataset,
$\mathrm{P\small{ARA}\normalsize{MAWPS}}$, consisting of paraphrased,
adversarial, and inverse variants of selectively sampled MWPs from the
benchmark $\mathrm{M\small{AWPS}}$ dataset. We extensively experiment on this
dataset along with other benchmark datasets using some baseline MWP solver
models. We show that training on linguistic variants of problem statements and
voting on candidate predictions improve the mathematical reasoning and
robustness of the model. We make our code and data publicly available.
