Qualitative relationships illustrate how changing one property (e.g., moving velocity) affects another (e.g., kinetic energy) and constitutes a considerable portion of textual knowledge. Current approaches use either semantic parsers to transform natural language inputs into logical expressions or a "black-box" model to solve them in one step. The former has a limited application range, while the latter lacks interpretability. In this work, we categorize qualitative reasoning tasks into two types: prediction and comparison. In particular, we adopt neural network modules trained in an end-to-end manner to simulate the two reasoning processes. Experiments on two qualitative reasoning question answering datasets, QuaRTz and QuaRel, show our methods' effectiveness and generalization capability, and the intermediate outputs provided by the modules make the reasoning process interpretable.

翻译：定性关系说明一个属性的变化(如移动速度)如何影响另一个属性(如动能),并构成文本知识的相当一部分。当前的方法要么使用语义分析器将自然语言输入转换成逻辑表达式,要么用“黑箱”模型一步解决。前者的应用范围有限,而后者缺乏可解释性。在这项工作中,我们将定性推理任务分为两类:预测和比较。特别是,我们采用了经过培训的神经网络模块,以模拟两个推理过程。关于两个质量推理问题回答数据集的实验,QuaRTz 和 QuaRel,展示了我们的方法的有效性和通用能力,以及模块提供的中间输出使推理过程可以解释。