论文标题
文本增强的对比度学习用于解决数学单词问题
Textual Enhanced Contrastive Learning for Solving Math Word Problems
论文作者
论文摘要
解决数学单词问题是分析数量关系的任务，需要准确了解上下文自然语言信息。最近的研究表明，当前的模型依靠浅启发式方法来预测解决方案，并且很容易被小型文本扰动误导。为了解决这个问题，我们提出了一个文本增强的对比度学习框架，该框架强制执行模型以区分语义上相似的示例，同时拥有不同的数学逻辑。我们采用一种自我监督的方式策略来通过文本重新排序或重新构建问题来丰富文本差异的示例。然后，我们检索最难将样本与方程式和文本角度区分开来，并指导模型以了解其表示形式。实验结果表明，我们的方法在广泛使用的基准数据集以及用英语和中文设计的精心设计的挑战数据集上实现了最先进的方法。 \ footNote {我们的代码和数据可在\ url {https://github.com/yiyunya/textual_cl_mwp}获得
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}
