论文标题
实践使求解器完美：数学单词问题求解器的数据增加
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers
论文作者
论文摘要
现有的数学单词问题（MWP）求解器在基准数据集上实现了很高的精度。但是，先前的工作表明，这样的求解器不能很好地概括，并且依靠表面提示来实现高性能。在本文中，我们首先进行实验，以证明这种行为主要与现有MWP数据集中存在的大小和多样性有限有关。接下来，我们建议将几种数据增强技术广泛归类为基于替代的方法。通过部署这些方法，我们将现有数据集的大小增加了五倍。在三个最先进的MWP求解器上的两个基准数据集上进行了广泛的实验表明，提出的方法会增加现有求解器的概括和鲁棒性。平均而言，提议的方法将最新结果显着增加了基准数据集上的5个百分点以上。此外，在增强数据集中训练的求解器在挑战测试集上的性能相对较好。我们还通过消融研究来展示提出的技术的有效性，并通过人类评估来验证增强样品的质量。
Existing Math Word Problem (MWP) solvers have achieved high accuracy on benchmark datasets. However, prior works have shown that such solvers do not generalize well and rely on superficial cues to achieve high performance. In this paper, we first conduct experiments to showcase that this behaviour is mainly associated with the limited size and diversity present in existing MWP datasets. Next, we propose several data augmentation techniques broadly categorized into Substitution and Paraphrasing based methods. By deploying these methods we increase the size of existing datasets by five folds. Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers show that proposed methods increase the generalization and robustness of existing solvers. On average, proposed methods significantly increase the state-of-the-art results by over five percentage points on benchmark datasets. Further, the solvers trained on the augmented dataset perform comparatively better on the challenge test set. We also show the effectiveness of proposed techniques through ablation studies and verify the quality of augmented samples through human evaluation.
