Textual Enhanced Contrastive Learning for Solving Math Word Problems

TL;DR

This paper introduces a Textual Enhanced Contrastive Learning framework that improves math word problem solving by better distinguishing semantically similar but logically different examples, leading to state-of-the-art results.

Contribution

The paper proposes a novel contrastive learning approach that uses textual reordering and problem reconstruction to enhance model understanding of math problems.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively distinguishes semantically similar but logically different problems.

Improves robustness against textual perturbations.

Abstract

Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual perturbations. To address this problem, we propose a Textual Enhanced Contrastive Learning framework, which enforces the models to distinguish semantically similar examples while holding different mathematical logic. We adopt a self-supervised manner strategy to enrich examples with subtle textual variance by textual reordering or problem re-construction. We then retrieve the hardest to differentiate samples from both equation and textual perspectives and guide the model to learn their representations. Experimental results show that our method achieves state-of-the-art on both widely used benchmark datasets and also exquisitely designed challenge datasets in English and Chinese. \footnote{Our code and data is available at \url{https://github.com/yiyunya/Textual_CL_MWP}