论文标题
改善数学单词问题解决中的组成概括
Improving Compositional Generalization in Math Word Problem Solving
论文作者
论文摘要
组成概括是指模型可以根据训练过程中观察到的数据组件概括为新组成的输入数据的能力。它触发了对不同任务的一系列组成概括分析，因为概括是语言和解决问题技能的重要方面。但是，关于数学单词问题（MWP）的类似讨论受到限制。在此手稿中，我们研究了MWP求解中的组成概括。具体来说，我们首先引入了一种数据拆分方法，以创建现有MWP数据集的组合分解。同时，我们合成数据以隔离组成的效果。为了改善MWP解决方案中的组成概括，我们提出了一种迭代数据增强方法，该方法将各种组成变化包括在培训数据中，并可以与MWP方法合作。在评估过程中，我们检查了一组方法，发现所有方法都会在评估的数据集中遇到严重的性能损失。我们还发现我们的数据增强方法可以显着改善一般MWP方法的组成概括。代码可在https://github.com/demoleiwang/cgmwp上找到。
Compositional generalization refers to a model's capability to generalize to newly composed input data based on the data components observed during training. It has triggered a series of compositional generalization analysis on different tasks as generalization is an important aspect of language and problem solving skills. However, the similar discussion on math word problems (MWPs) is limited. In this manuscript, we study compositional generalization in MWP solving. Specifically, we first introduce a data splitting method to create compositional splits from existing MWP datasets. Meanwhile, we synthesize data to isolate the effect of compositions. To improve the compositional generalization in MWP solving, we propose an iterative data augmentation method that includes diverse compositional variation into training data and could collaborate with MWP methods. During the evaluation, we examine a set of methods and find all of them encounter severe performance loss on the evaluated datasets. We also find our data augmentation method could significantly improve the compositional generalization of general MWP methods. Code is available at https://github.com/demoleiwang/CGMWP.
