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Math Word Problems (MWP) aims to automatically solve mathematical questionsgiven in texts. Previous studies tend to design complex models to captureadditional information in the original text so as to enable the model to gainmore comprehensive features. In this paper, we turn our attention in theopposite direction, and work on how to discard redundant features containingspurious correlations for MWP. To this end, we design an Expression SyntaxInformation Bottleneck method for MWP (called ESIB) based on variationalinformation bottleneck, which extracts essential features of expression syntaxtree while filtering latent-specific redundancy containing syntax-irrelevantfeatures. The key idea of ESIB is to encourage multiple models to predict thesame expression syntax tree for different problem representations of the sameproblem by mutual learning so as to capture consistent information ofexpression syntax tree and discard latent-specific redundancy. To improve thegeneralization ability of the model and generate more diverse expressions, wedesign a self-distillation loss to encourage the model to rely more on theexpression syntax information in the latent space. Experimental results on twolarge-scale benchmarks show that our model not only achieves state-of-the-artresults but also generates more diverse solutions. The code is available.

Expression Syntax Information Bottleneck for Math Word Problems