Math word problems (MWPs) is a task that automatically derives solution
expression from a giving math problems in text. The previous studies suffer
from spurious correlations between input text and output expression. To
mitigate this issue, we propose a self-consistent reasoning framework called
SCR, which attempts to adopt a pruning strategy to correct the output
distribution shift so as to implicitly fix those spurious correlative samples.
Specifically, we firstly obtain a sub-network by pruning a roberta2tree model,
for the sake to use the gap on output distribution between the original
roberta2tree model and the pruned sub-network to expose spurious correlative
samples. Then, we calibrate the output distribution shift by applying symmetric
Kullback-Leibler divergence to alleviate spurious correlations. In addition,
SCR generates equivalent expressions, thereby, capturing the original text's
logic rather than relying on hints from original text. Extensive experiments on
two large-scale benchmarks demonstrate that our model substantially outperforms
the strong baseline methods.Comment: Submitted to IEEE ICASSP 202